# Networks in Archaeology: Phenomena, Abstraction, Representation

- 2.3k Downloads
- 33 Citations

## Abstract

The application of method and theory from network science to archaeology has dramatically increased over the last decade. In this article, we document this growth over time, discuss several of the important concepts that are used in the application of network approaches to archaeology, and introduce the other articles in this special issue on networks in archaeology. We argue that the suitability and contribution of network science techniques within particular archaeological research contexts can be usefully explored by scrutinizing the past phenomena under study, how these are abstracted into concepts, and how these in turn are represented as network data. For this reason, each of the articles in this special issue is discussed in terms of the phenomena that they seek to address, the abstraction in terms of concepts that they use to study connectivity, and the representations of network data that they employ in their analyses. The approaches currently being used are diverse and interdisciplinary, which we think are evidence of a healthy exploratory stage in the application of network science in archaeology. To facilitate further innovation, application, and collaboration, we also provide a glossary of terms that are currently being used in network science and especially those in the applications to archaeological case studies.

## Keywords

Archaeology Network science Social network analysis Relational archaeology## Notes

### Acknowledgments

The authors are grateful to the contributors to this special issue and to all speakers at the session of the Society of American Anthropology in Hawaii in 2013 from which this special issue is derived, including the discussant Ian Hodder. We would also like to thank Catherine Cameron and James Skibo for their help in compiling and editing the contributions.

## Glossary

*e.g.*, 1–2 indicates that node one is connected to node 2). A key reference work for most of the concepts described here is that by Wasserman and Faust (1994), in which more elaborate descriptions, mathematical formulations, and additional bibliographic resources can be found. A limited number of additional primary sources are given in this glossary and included in a separate bibliography below.

The glossary presented here benefited greatly from discussions with members of the algorithmics group at the Department of Computer and Information Science of the University of Konstanz, John M. Roberts Jr., and the contributors to this special issue. It is coauthored with Habiba. The authors of this paper are solely responsible for any remaining mistakes in this glossary.

See node.

Defined as a directed network with no cycles.

For example, the directed network in Fig. 5b is not acyclic because it includes the cycle 2-3-4-2. Examples of acyclic networks include citation networks and dendrograms.

Defined as a way of representing a network where there is a row and a column for each node, and the values in the cells indicate whether an edge exists between a pair of nodes.

See two-mode network.

See directed edge.

See degree.

See geodesic.

A node’s betweenness centrality is defined as the fraction of the number of geodesics passing through this node over the number of geodesics between all pairs of nodes in the network.

Nodes with a high betweenness centrality are often considered to be important intermediaries for controlling the flow of resources between other nodes, because they are located on paths between many other node pairs. The concept of brokerage is often mentioned in relation to betweenness centrality. Nodes which are incident to the *only* edge connecting two subsets of nodes in the network are in a position to broker the relationship between these nodes. These nodes will typically have a high betweenness centrality but not necessarily a high degree or closeness centrality. For example, in Fig. 4 if the only route between towns A and C travels through B, town B may be in a position to tax any goods traveling between them, to control what information travels between towns, or to isolate one or both. Betweenness centrality was first quantitatively expressed by Anthonisse (1971) and Freeman (1977).

See two-mode networks.

Defined as a partitioning into blocks of structurally equivalent nodes, where the blocks are connected by hypothesized edges.

In blockmodeling, the rows and columns of adjacency matrices are arranged so that structurally equivalent nodes are in adjacent positions in the matrix, and the edges between different blocks can be studied. First introduced by White, Boorman, and Breiger (1976).

See betweenness centrality.

Defined as a family of measures of the node’s position within the network, which represent a ranking of nodes (see betweenness, closeness, degree, and eigenvector centralities).

Centrality measures are used to identify the most important or prominent nodes in the network, depending on the different definitions of importance or prominence implemented in the network measure used.

A clique is a subset of nodes in a network, where every pair of nodes is connected by an edge.

In the social sciences, only cliques of three nodes or more are usually considered. For example, in Fig. 5a, nodes 2, 3, and 4 form a clique of size 3. The definition of a clique is independent of whether it applies to the whole network or not. For example, a network can consist of multiple cliques, or an entire network can be one clique. The latter can also be called a complete network.

The closeness centrality of a node is defined as the inverse of the sum of the geodesics of that node to all other nodes divided by the number of nodes in the network.

The closeness centrality of a node gives an indication of how close this node is to all other nodes in the network, represented as the number of steps in the network that are necessary on average to reach another node. Nodes with a high closeness centrality score could be considered important or prominent, since they can share and obtain resources in less steps than other nodes. Early quantitative implementations of closeness centrality are reviewed by Freeman (1979).

Defined as the number of closed triplets over the total number of triplets in a network, where a triplet is a set of three nodes with two (open triplet) or three (closed triplet) undirected edges between them.

The clustering coefficient represents the average probability that two nodes connected to a third node are themselves connected, and it is commonly used for this purpose since the publication of the “small-world” network model (Watts and Strogatz 1998). The clustering coefficient of a network is closely related to the concept of transitivity in the social sciences, which captures the notion that “a friend of a friend is a friend.” Transitivity refers to the tendency of an open triplet to become a closed triplet.

See density.

See clique.

Defined as a subset of an undirected network in which any pair of nodes can be connected to each other *via* at least one path, and where there can be no paths to any nodes outside this subset.

For example, in the undirected network in Fig. 5a, there are two connected components: node 5 and nodes 1, 2, 3, and 4.

See edge.

Defined as a path in a directed network in which the starting node and ending node are the same. It is also called a closed path.

For example, in Fig. 5b, the path 2-3-4-2 is a cycle. See also acyclic network.

The degree of a node is defined as the number of edges incident to this node.

The average degree of a network is the sum of the degrees of all nodes in this network divided by the number of nodes. In a directed network, the indegree of a node refers to the number of incoming incident edges of a node. In a directed network, the outdegree of a node refers to the number of outgoing incident edges of a node. For example, node 2 in Fig. 5a has a degree of 3, while the same node can be said in Fig. 5b to have an indegree of 2 and an outdegree of 1.

Defined as the centrality of a node based on the number of edges incident to this node.

According to the degree centrality measure, a node is important or prominent if it has edges to a high number of other nodes.

Defined as the probability distribution of all degrees over the whole network.

The measure is commonly used to compare the structure of networks since the publication of the “scale-free” network structure (Albert and Barabási 2002, p. 49; Barabási and Albert 1999; Newman 2010, p. 243–247). In scale-free networks, the degree distribution follows a power-law.

Defined as the fraction of the number of edges that are present to the maximum possible number of edges in the network.

Cohesion is a commonly used concept which is often operationalized using the density measure. For other cohesion measures, see Wasserman and Faust (1994, 249–290).

Defined as the length of the longest geodesic in the network.

Defined as an ordered pair of nodes, which is often graphically represented as an arrow drawn from a starting node to an end node.

A directed edge is asymmetric. It connects a starting node with an ending node and cannot be traversed in the other direction. For example, all edges in Fig. 5b are directed edges.

Defined as a set of nodes and a set of directed edges.

A path through a directed network will need to follow the direction of the directed edges. For example, the network in Fig. 5b is a directed network.

See path length.

Defined as any pair of nodes in a network that may or may not have an edge between them.

For undirected edges, there are two possible dyadic relationships: connected, or not connected. For directed edges, there are four: unconnected; connected in one direction; connected in the other direction; and connected in both directions.

Defined as a line between a pair of nodes, representing some kind of relationship between them.

Many synonyms exist to refer to an edge, including tie, arc, relationship, link, connection, and line. An edge can be directed or undirected, and weighted or unweighted. The concept “arc” is often used to refer to a directed edge.

Defined as a network consisting of a node (called ego), the nodes it is directly connected to, and the edges between these nodes.

The eigenvector centrality of a node is defined in terms of the eigenvector centrality of nodes incident on it.

More descriptively, instead of assigning a single centrality score to a node, a node’s eigenvector centrality is defined in terms proportional to the nodes incident on it. A node with a high eigenvector centrality is a node that is connected to other nodes with a high eigenvector centrality. See Newman (Newman 2010, 169–172) for the procedure to calculate eigenvector centrality.

A polyvalent concept that comprises two variants. The first is the structural integration of a node or any group of nodes within the network. Different measures for structural integration exist. The E/I index is one example of this, which is calculated as a ratio of the number of edges within a group of nodes (internal) and between groups of nodes (external). The second variant, as popularized in economic theory through the work of Polanyi (1944) and Granovetter (1985), relates to the intertwined nature of social, economic, political, religious, and cultural interactions. See Borck *et al.* (2015) and Hess (2004) for overviews of this concept.

See structural equivalence.

Defined as the path between a pair of nodes with the shortest path length. Sometimes referred to as the shortest path length between a pair of nodes. For example, the geodesic between nodes 1 and 3 in Fig. 5b is the path 1–3 with a length of 1. The average shortest path length is the average of all geodesics in a network.

See network.

Defined as a tendency of nodes to become connected to other nodes that are dissimilar under a certain definition of dissimilarity.

Defined as a tendency of nodes to become connected to other nodes that are similar under a certain definition of similarity.

For example, in Fig. 6b, a node with an attribute value represented in gray will have a tendency of being connected to a node with the same attribute value.

See degree.

Defined as nodes in a network which have no incident edges.

For example, node 5 in Fig. 5a is an isolate.

See edge.

See edge.

Defined as a set of nodes and a set of edges.

In mathematics, a network is referred to as a graph, while networks often consist of social nodes and edges, and are referred to as social networks in the social sciences.

Defined as an atomic discrete entity representing a network concept.

A vertex (plural vertices) is a commonly used synonym to refer to a node. The term actor is sometimes used as a synonym for nodes in the social sciences.

See two-mode network.

See degree.

Defined as a walk between a pair of nodes in which no nodes and edges are repeated.

For example, nodes 1 and 3 in Fig. 5b are connected by the path 1-2-3.

Defined as the number of edges in a path.

For example, nodes 1 and 3 in Fig. 5b are connected by the path 1-2-3, which has a path length of 2.

Defined as a mathematical relationship between two entities where the frequency of one entity varies as a power of the second entity. More formally, the probability of a node with degree *k* is proportional to *k* ^{ a }.

Commonly used to describe the degree distribution of networks with a scale-free structure (Barabási and Albert 1999). When a network’s degree distribution follows a power-law, it implies that few nodes have a much higher degree than all other nodes in the network and most nodes have a very low degree. Nodes with a very high degree, sometimes referred to as “hubs” in the network, significantly reduce the average shortest path length of the network.

See edge.

See geodesic.

A small-world network is defined as a network in which the average shortest path length is almost as small as that of a uniformly random network with the same number of nodes and density, whereas the clustering coefficient is much higher than in a uniformly random network (a uniformly random network is defined as a network in which each edge exists with a fixed probability *p*).

The small-world network structure as described here was first published by Watts and Strogatz (1998). This structure illustrates that relatively few edges between clusters of nodes are needed to significantly reduce the average shortest path length. It implies that resources can flow between any pairs of nodes in the network relatively efficiently, while maintaining a high degree of clustering.

See network.

Defined as a connected component in a directed network.

In a directed network, a connected component is always either strongly or weakly connected. For example, in Fig. 5b, node 5 is a connected component, while the set of nodes 1, 2, 3, and 4 are not because node 1 cannot be reached by a path from the other nodes.

A number of theoretical network models used in the social sciences rely on a distinction between strong and weak ties (particularly those drawing on Granovetter 1973). The distinctions between the two, however, are rarely formally defined. In general, strong ties are used to describe frequently activated relationships (such as family/kin ties) whereas weak ties are used to describe infrequently accessed connections (acquaintances). Strong ties tend to be among actors with similar sets of overlapping relationships whereas weak ties more often connect sets of actors who would otherwise be unconnected. In weighted networks, thresholds on the distribution of weights across a network as a whole are often used to define strong *vs.* weak ties though there are no consistent rules used for this distinction.

Defined as two nodes are structurally equivalent if they have identical edges to and from all other nodes in the network (see Lorrain and White 1971).

Structural equivalence is used to identify nodes which have the same position in a network. It can be used to inform blockmodeling. In the social sciences, the structural similarities identified through structural equivalence are used to study social positions and social roles.

See edge.

See clustering coefficient.

Defined as a network in which two sets of nodes are defined as modes. In a two-mode network, nodes of one mode can only be connected to nodes of another node.

Defined as an unordered pair of nodes, which is often graphically represented as a line drawn between the pair of nodes.

An undirected edge is symmetric. Typically just called an edge. For example, all edges in Fig. 5a are undirected edges.

A set of nodes and a set of undirected edges.

An undirected network is symmetric. For example, the network in Fig. 5a is an undirected network.

Defined as an edge which is not weighted.

Typically just called an edge. See also weighted edge.

Defined as a set of nodes and a set of unweighted edges.

See weighted network.

See node.

A walk between a pair of nodes is defined as any sequence of nodes connected through edges which has that pair of nodes as endpoints.

In contrast to a path, nodes and edges can be repeated in a walk. For example, in Fig. 5b, a walk between nodes 2 and 3 could be 2-3-4-2-3.

Defined as a connected component in a directed network where the directionality of edges is ignored.

In a directed network, a connected component is always either strongly or weakly connected. For example, in Fig. 5b, there are two weakly connected components: node 5 and nodes 1, 2, 3, and 4.

See strong tie.

Defined as an edge with a value associated to it.

These values are often real numbers but they can also be any concept connecting the end nodes of the edge. The definition of an edge weight depends on the research context. Weights could be represented as an attribute of an edge. Thresholding can be applied to select a subset of edges with a given edge weight.

Defined as a set of nodes and a set of weighted edges.

## References

- Albert, R., & Barabási, A. (2002). Statistical mechanics of complex networks.
*Reviews of Modern Physics, 74*(January), 47–97.Google Scholar - Anthonisse, J. M. (1971).
*The rush in a graph*. Amsterdam: Mathematische Centrum.Google Scholar - Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks.
*Science, 286*(5439), 509–512. doi: 10.1126/science.286.5439.509.Google Scholar - Borck, L., Mills, B. J., Peeples, M. A., & Clark, J. J. (2015). Are social networks survival networks? An example from the Late Prehispanic U.S. Southwest.
*Journal of Archaeological Method and Theory ,22*(1). doi: 10.1007/s10816-014-9236-5. - Borgatti, B., Everett, M. G., & Johnson, J. C. (2013).
*Analyzing social networks*. Los Angeles: Sage.Google Scholar - Brandes, U., Robins, G., McCranie, A., & Wasserman, S. (2013). What is network science?
*Network Science, 1*(01), 1–15. doi: 10.1017/nws.2013.2.Google Scholar - Brughmans, T. (2013). Networks of networks: a citation network analysis of the adoption, use and adaptation of formal network techniques in archaeology.
*Literary and Linguistic Computing, The Journal of Digital Scholarship in the Humanities, 28*(4), 538–562.Google Scholar - Brughmans, T. (2014). The roots and shoots of archaeological network analysis: a citation analysis and review of the archaeological use of formal network methods.
*Archaeological Review from Cambridge, 29*(1), 18–41.Google Scholar - Brughmans, T., Collar, A., & Coward, F. (2015). Introduction: challenging network perspectives on the past. In T. Brughmans, A. Collar, & F. Coward (Eds.),
*The connected past: challenges to network studies of the past*. Oxford: Oxford University Press.Google Scholar - Brughmans, T., Keay, S., & Earl, G. P. (2015). Understanding inter-settlement visibility in Iron Age and Roman Southern Spain with exponential random graph models for visibility networks.
*Journal of Archaeological Method and Theory, 22*(1). doi: 10.1007/s10816-014-9231-x. - Crabtree, S. (2015). Inferring ancestral Pueblo social networks from simulation in the Central Mesa Verde.
*Journal of Archaeological Method and Theory, 22*(1). doi: 10.1007/s10816-014-9233-8. - Fenn, J., & Raskino, M. (2008).
*Mastering the hype cycle. How to choose the right innovation at the right time*. Boston: Harvard Business Press.Google Scholar - Freeman, L. C. (1977). A set of measures of centrality based on betweenness.
*Sociometry, 40*(1), 35–41.Google Scholar - Freeman, L. C. (1979). Centrality in social networks. I. Conceptual Clarification.
*Social Networks, 1*, 215–239.Google Scholar - Geertz, C. (1973). Thick description: toward an interpretive theory of culture. In C. Geertz (Ed.),
*The interpretation of cultures: Selected essays*(pp. 3–30). New York: Basic Books.Google Scholar - Gjesfjeld, E. (2015). Network Analysis of archaeological data from Hunter-Gatherers: methodological problems and potential solutions.
*Journal of Archaeological Method and Theory, 22*(1). doi: 10.1007/s10816-014-9232-9. - Golitko, M., & Feinman, G. M. (2015). Procurement and distribution of Pre-Hispanic Mesoamerican Obsidian 900 BC–AD 1520: a social network analysis.
*Journal of Archaeological Method and Theory, 22*(1). doi: 10.1007/s10816-014-9211-1. - Graham, S., & Weingart, S. (2015). The Equifinality of archaeological networks: an agent-based exploratory lab approach.
*Journal of Archaeological Method and Theory, 22*(1). doi: 10.1007/s10816-014-9230-y. - Granovetter, M. (1973). The strength of weak ties.
*American Journal of Sociology, 78*(6), 1360–1380.Google Scholar - Granovetter, M. (1985). Economic action and social structure: the problem of embeddedness.
*The American Journal of Sociology, 91*(3), 481–510.Google Scholar - Hess, M. (2004). “Spatial” relationships? Towards a reconceptualization of embeddedness.
*Progress in Human Geography, 28*(2), 165–186.Google Scholar - Hodder, I. (2011). Human-thing entanglement: towards an integrated archaeological perspective.
*The Journal of the Royal Anthropological Institute, 17*(1), 154–177.Google Scholar - Hodder, I. (2012).
*Entangled: An archaeology of the relationships between humans and things*. Oxford: Wiley-Blackwell.Google Scholar - Isaksen, L. (2013). “O what a tangled web we weave”—Towards a practice that does not deceive. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 43–70). Oxford: Oxford University Press.Google Scholar - Knappett, C. (2011).
*An archaeology of interaction: Network perspectives on material culture and society*. Oxford: Oxford University Press.Google Scholar - Knappett, C. (Ed.). (2013).
*Network analysis in archaeology: New approaches to regional interaction*. Oxford: Oxford University Press.Google Scholar - Knappett, C. (2015). Networks in archaeology: between scientific method and humanistic metaphor. In T. Brughmans, A. Collar, & F. Coward (Eds.),
*The connected past: Challenges to network studies of the past*. Oxford: Oxford University Press. (in press)Google Scholar - Latour, B. (2005).
*Reassembling the social: An introduction to actor-network-theory*. Oxford: Oxford University Press.Google Scholar - Lorrain, F., & White, H. C. (1971). Structural equivalence of individuals in social networks.
*Journal of Mathematical Sociology, 1*, 49–80.Google Scholar - Mills, B. J., Borck, L., Clark, J. J., Haas, Jr., W. R., Peeples, M. A., & Roberts, Jr. J. M. (2014). Multiscalar perspectives on Southwest social networks, A.D. 1200–1450.
*American Antiquity*.Google Scholar - Mills, B. J., Clark, J. J., Peeples, M. A., Haas, W. R., Roberts, J. M., Hill, J. B., Shackley, M. S. (2013). Transformation of social networks in the late pre-Hispanic US Southwest.
*Proceedings of the National Academy of Sciences of the United States of America*, 1–6. doi: 10.1073/pnas.1219966110 - Mol, A. A. A., Hoogland, M., & Hofman, C. L. (2015). Remotely local: ego-networks of late pre-colonial (AD 1000–1450) Saba, Northeastern Caribbean.
*Journal of Archaeological Method and Theory, 22*(1). doi: 10.1007/s10816-014-9234-7. - Newman, M. E. J. (2010).
*Networks: An introduction*. Oxford: Oxford University Press.Google Scholar - Östborn, P., & Gerding, H. (2015). The diffusion of fired bricks in Hellenistic Europe: a similarity network analysis.
*Journal of Archaeological Method and Theory, 22*(1). doi: 10.1007/s10816-014-9229-4. - Peeples, M. A., Mills, B. J., Roberts, J. M., Jr., Clark, J. J., & Haas, W. R., Jr. (2014). Analytical issues in the application of network analyses to archaeology. In T. Brughmans, A. Collar, & F. Coward (Eds.),
*The connected past: Challenges to network studies of the past*. Oxford: Oxford University Press.Google Scholar - Peeples, M. A., & Roberts, J. M., Jr. (2013). To binarize or not to binarize: relational data and the construction of archaeological networks.
*Journal of Archaeological Science, 40*(7), 3001–3010.Google Scholar - Polanyi, K. (1944).
*The great transformation. The political and economic origins of our time*. Boston: Beacon.Google Scholar - Scott, J., & Carrington, P. J. (2011).
*The SAGE handbook of social network analysis*. London: Sage.Google Scholar - Wasserman, S., & Faust, K. (1994).
*Social network analysis: Methods and applications*. Cambridge: Cambridge University Press.Google Scholar - Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks.
*Nature, 393*(6684), 440–442. doi: 10.1038/30918.Google Scholar - White, H. C., Boorman, S. A., & Breiger, R. L. (1976). Social structure from multiple networks. I. Blockmodels of roles and positions.
*American Journal of Sociology, 81*(4), 730–779.Google Scholar

## References included in Fig. 2

- Alexander, C. (2008). The Bedolina map: an exploratory network analysis. In A. Posluschny, K. Lambers, & I. Herzog (Eds.),
*Layers of perception: Proceedings of the 35th international conference on computer applications and quantitative methods in archaeology (CAA), Berlin, Germany, April 2–6, 2007. (Kolloquien zur Vor- und Frühgeschichte, Vol. 10)*(pp. 366–371). Bonn: Dr. Rudolf Habelt GmbH.Google Scholar - Allen, K. M. S. (1990). Modelling early historic trade in the eastern Great Lakes using geographic information systems. In K. M. S. Allen, S. W. Green, & E. B. W. Zubrow (Eds.),
*Interpreting space: GIS and archaeology*(pp. 319–329). London - New York - Philadelphia: Taylor & Francis.Google Scholar - Barthélemy, M. (2014). Discussion: social and spatial networks.
*Nouvelles de l’archéologie, 135*, 51–61.Google Scholar - Bentley, R. A., & Maschner, H. D. G. (2003).
*Complex systems and archaeology*. Salt Lake City: University of Utah Press.Google Scholar - Bentley, R. A., & Maschner, H. D. G. (2007). Complexity theory. In R. A. Bentley, H. D. G. Maschner, & C. Chippendale (Eds.),
*Handbook of archaeological theories*(pp. 245–270). AltaMira Press. doi: 10.1016/j.ijnurstu.2010.09.012. - Bentley, R. A., & Shennan, S. J. (2003). Cultural transmission and stochastic network growth.
*American Antiquity, 68*(3), 459–485.Google Scholar - Bentley, R. A., & Shennan, S. J. (2005). Random copying and cultural evolution.
*Science, 309*, 877–878. doi: 10.1126/science.309.5736.874b.Google Scholar - Bentley, R., Lake, M., & Shennan, S. (2005). Specialization and wealth inequality in a model of a clustered economic network.
*Journal of Archaeological Science, 32*(9), 1346–1356. doi: 10.1016/j.jas.2005.03.008.Google Scholar - Bernardini, W. (2007). Jeddito Yellow Ware and Hopi social networks.
*Kiva, 72*(3), 295–328.Google Scholar - Bevan, A., & Crema, E. R. (2014). Une modélisation géographiquement explicite d’interaction culturelle. Dialectes crétois modernes, archéologie de l'âge du Bronze.
*Nouvelles de l’archéologie, 135*, 45–50.Google Scholar - Bevan, A., & Wilson, A. (2013). Models of settlement hierarchy based on partial evidence.
*Journal of Archaeological Science, 40*(5), 2415–2427. doi: 10.1016/j.jas.2012.12.025.Google Scholar - Bintliff, J. (2004). Time, structure, and agency: the Annales, emergent complexity, and archaeology. In J. Bintliff (Ed.),
*A companion to archaeology*(pp. 174–194). Oxford: Blackwell.Google Scholar - Blake, E. (2013). Social networks, path dependence, and the rise of ethnic groups in pre-Roman Italy. In C. Knappett (Ed.),
*Network analysis in archaeology: new approaches to regional interaction*(pp. 203–222). Oxford: Oxford University Press.Google Scholar - Blake, E. (2014). Dyads and triads in community detection: a view from the Italian Bronze Age.
*Nouvelles de l’archéologie, 135*, 28–31.Google Scholar - Branting, S. (2007). Using an urban street network and a PGIS-T approach to analyze ancient movement. In E. M. Clark, J. T. Hagenmeister (Ed.),
*Digital discovery: Exploring new frontiers in human heritage. Proceedings of the 34th CAA conference, Fargo, 2006*(p. 87–96). Budapest.Google Scholar - Broodbank, C. (2000).
*An island archaeology of the early Cyclades*. Cambridge: Cambridge University Press.Google Scholar - Brughmans, T. (2010). Connecting the dots: towards archaeological network analysis.
*Oxford Journal of Archaeology, 29*(3), 277–303. doi: 10.1111/j.1468-0092.2010.00349.x.Google Scholar - Brughmans, T. (2012). Facebooking the past: A critical social network analysis approach for archaeology. In A. Chrysanthi, M. P. Flores, & C. Papadopoulos (Eds.),
*Thinking beyond the tool: Archaeological computing and the interpretative process. British Archaeological Reports International Series*(pp. 191–203). Oxford: Archaeo press.Google Scholar - Brughmans, T. (2013a). Thinking through networks: a review of formal network methods in archaeology.
*Journal of Archaeological Method and Theory, 20*, 623–662. doi: 10.1007/s10816-012-9133-8.Google Scholar - Brughmans, T. (2013b). Networks of networks: a citation network analysis of the adoption, use and adaptation of formal network techniques in archaeology.
*Literary and Linguistic Computing, The Journal of Digital Scholarship in the Humanities, 28*(4), 538–562. doi: 10.1093/llc/fqt048.Google Scholar - Brughmans, T. (2014). The roots and shoots of archaeological network analysis: a citation analysis and review of the archaeological use of formal network methods.
*Archaeological Review from Cambridge, 29*(1), 18–41.Google Scholar - Brughmans, T., Isaksen, L., & Earl, G. (2012). Connecting the dots: an introduction to critical approaches in archaeological network analysis. In M. Zhou, I. Romanowska, Z. Wu, P. Xu, & P. Verhagen (Eds.),
*Proceedings of computer applications and quantitative techniques in archaeology conference 2011, Beijing*(pp. 359–369). Amsterdam: Amsterdam University Press.Google Scholar - Brughmans, T., Keay, S., & Earl, G. (2012). Complex networks in archaeology: urban connectivity in Iron Age and Roman Southern Spain.
*Leonardo, 45*(3), 280.Google Scholar - Clarke, D. L. (1968).
*Analytical archaeology*. London: Methuen.Google Scholar - Classen, E. (2004). Verfahren der “Sozialen Netzwerkanalyse” und ihre Anwendung in der Archäologie.
*Archäologische Informationen, 27*, 219–226.Google Scholar - Classen, E. (2008). Early Neolithic social networks in Western Germany. In A. Posluschny, K. Lambers, & I. Herzog (Eds.),
*Layers of perception. Proceedings of the 35th international conference on computer applications and quantitative methods in archaeology (CAA), Berlin April 2-6 2007. Kolloquien zur Vor- und Frühgeschichte, Vol. 10*. Bonn: Dr. Rudolf Habelt GmbH. 372+CD-ROM.Google Scholar - Classen, E., & Zimmerman, A. (2004). Tessellation and triangulations: Understanding Early Neolithic social networks. In K. F. Ausserer (Ed.),
*Enter the past: proceedings of the 30th CAA conference held in Vienna, Austria, April 2003*. Oxford, 467–471.Google Scholar - Cochrane, E. E., & Lipo, C. (2010). Phylogenetic analyses of Lapita decoration do not support branching evolution or regional population structure during colonization of Remote Oceania.
*Proceedings of the Royal Society B, 365*, 3889–3902.Google Scholar - Collar, A. C. F. (2007). Network theory and religious innovation.
*Mediterranean Historical Review, 22*(1), 149–162. doi: 10.1080/09518960701539372.Google Scholar - Collar, A. C. F. (2008).
*Networks and religious innovation in the Roman Empire*. PhD thesis, University of Exeter.Google Scholar - Collar, A. C. F. (2013a).
*Religious networks in the Roman Empire: The spread of new ideas*. Cambridge: Cambridge University Press.Google Scholar - Collar, A. C. F. (2013b). Re-thinking Jewish ethnicity through social network analysis. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 223–246). Oxford: Oxford University Press.Google Scholar - Collar, A. C. F., Brughmans, T., Coward, F., & Lemercier, C. (2014). Analyser les réseaux du passé en archéologie et en histoire.
*Nouvelles de l’archéologie, 135*, 9–13.Google Scholar - Coward, F. (2010). Small worlds, material culture and ancient Near Eastern social networks.
*Proceedings of the British Academy, 158*, 449–479.Google Scholar - Coward, F. (2013). Grounding the net: social networks, material culture and geography in the Epipalaeolithic and early Neolithic of the Near East (~21–6,000 cal BCE). In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 247–280). Oxford: Oxford University Press.Google Scholar - Coward, F., & Gamble, C. (2008). Big brains, small worlds: material culture and the evolution of the mind.
*Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 363*(1499), 1969–1979. doi: 10.1098/rstb.2008.0004.Google Scholar - Doran, J. E., & Hodson, F. R. (1975).
*Mathematics and computers in archaeology*. Edinburgh: Edinburgh University Press.Google Scholar - Dunn, H. (2014). Population genetics and the investigation of past human interactions.
*Archaeological Review from Cambridge, 29*(1), 42–66.Google Scholar - Earl, G., & Keay, S. (2007). Urban connectivity of Iberian and Roman Towns in Southern Spain: a network analysis approach. In J. T. Clark, & E. M. Hagenmeister (Eds.)
*Digital discovery: Exploring new frontiers in human heritage. Proceedings of the 34th CAA conference, Fargo, 2006*(pp. 77–86).Google Scholar - Evans, S., & Felder, K. (2014). Making the connection: changing perspectives on social networks.
*Archaeological Review from Cambridge, 29*(1), 9–17.Google Scholar - Evans, T., Knappett, C., & Rivers, R. (2009). Using statistical physics to understand relational space: a case study from Mediterranean prehistory. In D. Lane, D. Pumain, S. Van Der Leeuw, & G. West (Eds.),
*Complexity perspectives in innovation*(pp. 451–479). Dordrecht: Springer.Google Scholar - Fulminante, F. (2012). Social network analysis and the emergence of central places: a case study from Central Italy (Latium Vetus).
*BABesch, 87*, 27–53.Google Scholar - Fulminante, F. (2014). The network approach: tool or paradigm?
*Archaeological Review from Cambridge, 29*(1), 167–178.Google Scholar - Gamble, C. (1999).
*The Palaeolithic societies of Europe*. Cambridge: Cambridge University Press.Google Scholar - Gjesfjeld, E., & Phillips, S. C. (2013). Evaluating adaptive network strategies with geochemical sourcing data: a case study from the Kuril Islands. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 281–306). Oxford: Oxford University Press.Google Scholar - Golitko, M., Meierhoff, J., Feinman, G. M., & Williams, P. R. (2012). Complexities of collapse: the evidence of Maya obsidian as revealed by social network graphical analysis.
*Antiquity, 86*, 507–523.Google Scholar - Graham, S. (2005). Agent-based modelling, archaeology and social organisation: the robustness of Rome.
*The Archaeological Computing Newsletter, 63*, 1–6.Google Scholar - Graham, S. (2006a).
*EX FIGLINIS, the network dynamics of the Tiber valley brick industry in the hinterland of Rome, BAR international series 1486*. Oxford: Archaeopress.Google Scholar - Graham, S. (2006b). Networks, agent-based models and the Antonine Itineraries: implications for Roman archaeology.
*Journal of Mediterranean Archaeology, 19*(1), 45–64.Google Scholar - Graham, S. (2006). Who’s in charge? Studying social networks in the Roman brick industry in Central Italy. In C. Mattusch & A. Donohue (Eds.),
*Common ground: Archaeology, art, science, and humanities—Proceedings of the XVI International Congress of Classical Archaeology, Oxford*(pp. 359–362).Google Scholar - Graham, S. (2009). The space between: the geography of social networks in the Tiber valley. In F. Coarelli & H. Patterson (Eds.),
*Mercator Placidissimus: the Tiber Valley in Antiquity, new research in the upper and middle river valley*. Edizioni Quasar: Rome.Google Scholar - Graham, S. (2014). On connecting stamps—network analysis and epigraphy.
*Nouvelles de l’archéologie, 135*, 39–44.Google Scholar - Graham, S., & Steiner, J. (2007). TravellerSim: growing settlement structures and territories with agent-based modeling. In J. T. Clark & E. M. Hagenmeister (Eds.),
*Digital discovery: exploring new frontiers in human heritage. Proceedings of the 34th CAA conference, Fargo, 2006*(pp. 57–67). Budapest: Archaeolingua.Google Scholar - Hage, P., & Harary, F. (1983).
*Structural models in anthropology*. Cambridge: Cambridge University Press.Google Scholar - Hage, P., & Harary, F. (1991).
*Exchange in Oceania: A graph theoretic analysis*. Oxford: Clarendon.Google Scholar - Hage, P., & Harary, F. (1996).
*Island networks: communication, kinship and classification structures in Oceania*. Cambridge: Cambridge University Press.Google Scholar - Hiorns, R. W. (1971). Statistical studies in migration. In F. R. Hodson, D. G. Kendall, & P. Tăutu (Eds.),
*Mathematics in the archaeological and historical sciences, Proceedings of the Anglo-Romanian conference, Mamaia 1970*(pp. 291–302). Edinburgh: Edinburgh University Press.Google Scholar - Hunt, T. L. (1988). Graph theoretic network models for Lapita exchange: a trial application. In P. V. Kirch & T. L. Hunt (Eds.),
*Archaeology of the Lapita cultural complex: A critical review*(pp. 135–155). Seattle: Thomas Burke Memorial Washington State Museum Research Reports no. 5.Google Scholar - Hutchinson, P. (1972). Networks and roman roads: a further Roman network.
*Area, 4*(4), 279–280.Google Scholar - Irwin, G. (1978). Pots and entrepôts: a study of settlement, trade and the development of economic specialization in Papuan prehistory.
*World Archaeology, 9*(3), 299–319.Google Scholar - Isaksen, L. (2007). Network analysis of transport vectors in Roman Baetica. In J. T. Clark & E. M. Hagenmeister (Eds.),
*Digital discovery: Exploring new frontiers in human heritage. Proceedings of the 34th CAA conference, Fargo, 2006*(pp. 76–87). Budapest: Archaeolingua.Google Scholar - Isaksen, L. (2008). The application of network analysis to ancient transport geography: a case study of Roman Baetica.
*Digital Medievalist*,*4*, http://www.digitalmedievalist.org/journal/4/isakse. - Isaksen, L. (2013). “O what a tangled web we weave”—Towards a practice that does not deceive. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 43–70). Oxford: Oxford University Press.Google Scholar - Jenkins, D. (2001). A network analysis of Inka roads, administrative centers, and storage facilities.
*Ethnohistory, 48*(4), 655–687.Google Scholar - Jiménez, D., & Chapman, D. (2002). An application of proximity graphs in archaeological spatial analysis. In D. Wheatley, G. Earl, & S. Poppy (Eds.),
*Contemporary themes in archaeological computing*(pp. 90–99). Oxford: Oxbow Books.Google Scholar - Kendall, D. (1969). Incidence matrices, interval graphs and seriation in archeology.
*Pacific Journal of Mathematics, 28*(3), 565–570.Google Scholar - Kendall, D. G. (1971a). Seriation from abundance matrices. In F. R. Hodson, D. G. Kendall, & P. Tăutu (Eds.),
*Mathematics in the archaeological and historical sciences, Proceedings of the Anglo-Romanian conference, Mamaia 1970*(pp. 215–252). Edinburgh: Edinburgh University Press.Google Scholar - Kendall, D. G. (1971b). Maps from marriages: an application of non-metric multi-dimensional scaling to parish register data. In F. R. Hodson, D. G. Kendall, & P. Tăutu (Eds.),
*Mathematics in the archaeological and historical sciences, Proceedings of the Anglo-Romanian conference, Mamaia 1970*(pp. 303–318). Edinburgh: Edinburgh University Press.Google Scholar - Knappett, C. (2011).
*An archaeology of interaction: Network perspectives on material culture and society*. Oxford: Oxford University Press.Google Scholar - Knappett, C. (2013a).
*Network analysis in archaeology: New approaches to regional interaction*. Oxford: Oxford University Press.Google Scholar - Knappett, C. (2013b). Introduction: why networks? In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 3–16). Oxford: Oxford University Press.Google Scholar - Knappett, C. (2014). Avant-propos. Dossier: Analyse des réseaux sociaux en archéologie.
*Nouvelles de l’archéologie, 135*, 5–8.Google Scholar - Knappett, C., Evans, T., & Rivers, R. (2008). Modelling maritime interaction in the Aegean Bronze Age.
*Antiquity, 82*(318), 1009–1024.Google Scholar - Knappett, C., Evans, T., & Rivers, R. (2011). The Theran eruption and Minoan palatial collapse: new interpretations gained from modelling the maritime network.
*Antiquity, 85*(329), 1008–1023.Google Scholar - Kohler, T. A. (2012). Complex systems and archaeology. In I. Hodder (Ed.),
*Archaeological theory today II*(pp. 93–123). Cambridge: Polity Press.Google Scholar - Leidwanger, J. (2014). Maritime networks and economic regionalism in the Roman Eastern Mediterranean.
*Nouvelles de l’archéologie, 135*, 32–38.Google Scholar - Lock, G., & Pouncett, J. (2007). Network analysis in archaeology session introduction: an introduction to network analysis. In J. T. Clark & E. M. Hagenmeister (Eds.),
*Digital discovery: exploring new frontiers in human heritage. Proceedings of the 34th CAA conference, Fargo, 2006*(pp. 71–73). Budapest: Archaeolingua.Google Scholar - Mackie, Q. (2001).
*Settlement archaeology in a Fjordland archipelago: Network analysis, social practice and the built environment of Western Vancouver Island, British Columbia, Canada since 2,000 BP. BAR international series 926*. Oxford: Archaeopress.Google Scholar - Menze, B. H., & Ur, J. A. (2012). Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale.
*Proceedings of the National Academy of Sciences of the United States of America, 109*(14), E778–E787. doi: 10.1073/pnas.1115472109.Google Scholar - Mills, B. J., Clark, J. J., Peeples, M. A., Haas, W. R., Roberts, J. M., Hill, J. B., Shackley, M. S. (2013). Transformation of social networks in the late pre-Hispanic US Southwest.
*Proceedings of the National Academy of Sciences of the United States of America*, 1–6. doi: 10.1073/pnas.1219966110 - Mills, B. J., Roberts, J. M., Clark, J. J., Jr., Haas, W. R., Huntley, D., Peeples, M. A., & Breiger, R. L. (2013b). In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 181–202). Oxford: Oxford University Press.Google Scholar - Mizoguchi, K. (2009). Nodes and edges: a network approach to hierarchisation and state formation in Japan.
*Journal of Anthropological Archaeology, 28*(1), 14–26. doi: 10.1016/j.jaa.2008.12.001.Google Scholar - Mizoguchi, K. (2014). Evolution of prestige good systems: An application of network analysis to the transformation of communication systems and their media. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 151–179). Oxford: Oxford University Press.Google Scholar - Mol, A. A. A. (2014). Play-things and the origins of online networks: virtual material culture in multiplayer games.
*Archaeological Review from Cambridge, 29*(1), 144–166.Google Scholar - Mol, A. A. A., & Mans, J. (2013). Old boy networks in the indigenous Caribbean. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 307–333). Oxford: Oxford University Press.Google Scholar - Müller, U. (2010). Zentrale Orte und Netzwerk Zwei Konzepte zur Beschreibung von Zentralität. In C. Dobiat, P. Ettel, & F. Fless (Eds.),
*Zwischen Fjorden und Steppe. Festschrift fur Johan Callmer zum 65. Geburtstag*(pp. 57–67). Rahden: Verlag Marie Leidorf GmbH.Google Scholar - Müller, U. (2012). Networks of towns—networks of periphery? Some relations between the North European Medieval town and its hinterland. In S. Brather, U. Müller, & H. Steuer (Eds.),
*Raumbildung durch Netzwerke? Der Ostseeraum zwischen Wikingerzeit un Spaetmittelalter aus archaeologischer und geschichtswissenschaftlicher Perspektive*(pp. 55–78). Bonn: Dr. Rudolf Habelt GmbH.Google Scholar - Munson, J. (2013). From metaphors to practice: operationalizing network concepts for archaeological stratigraphy.
*Journal of Archaeological Method and Theory*. doi: 10.1007/s10816-013-9181-8.Google Scholar - Munson, J. L., & Macri, M. J. (2009). Sociopolitical network interactions: a case study of the Classic Maya.
*Journal of Anthropological Archaeology, 28*(4), 424–438. doi: 10.1016/j.jaa.2009.08.002.Google Scholar - Nelson, E. S., & Kassabaum, M. C. (2014). Expanding social networks through ritual deposition: a case study from the Lower Mississippi Valley.
*Archaeological Review from Cambridge, 29*(1), 103–128.Google Scholar - Orser, C, Jr. (2005). Network theory and the archaeology of modern history. In P. Funari, A. Zarankin, & E. Stovel (Eds.)
*Global archaeological theory SE - 7*(pp. 77–95). Springer US. doi: 10.1007/0-306-48652-0_7. - Orton, C. (1980).
*Mathematics in archaeology*. London: Collins.Google Scholar - Östborn, P., & Gerding, H. (2014). Network analysis of archaeological data: a Systematic Approach.
*Journal of Archaeological Science*.Google Scholar - Peeples, M. A. (2011).
*Identity and social transformation in the prehispanic Cibola world: A.D. 1150-1325*. Ph.D. dissertation, Arizona State University.Google Scholar - Peeples, M. A., & Roberts, J. M. (2013). To binarize or not to binarize: relational data and the construction of archaeological networks.
*Journal of Archaeological Science, 40*(7), 3001–3010. doi: 10.1016/j.jas.2013.03.014.Google Scholar - Peregrine, P. (1991). A graph-theoretic approach to the evolution of Cahokia.
*American Antiquity, 56*(1), 66–75.Google Scholar - Phillips, S. C. (2011).
*Networked glass: Lithic raw material consumption and social networks in the Kuril islands*. University of Washington, Seattle: Far Eastern Russia. Unpublished PhD dissertation.Google Scholar - Pouncett, J., & Lock, G. (2007). A vector-based approach to the integration of geophysical and test-pitting data: Phasing the South Cadbury Environs Project. In E. M. Clark & J. T. Hagenmeister (Eds.),
- Riede, F. (2014). Eruptions and ruptures—a social network perspective on vulnerability and impact of the Laacher See eruption (c. 13,000 BP) on Late Glacial hunter-gatherers in northern Europe.
*Archaeological Review from Cambridge, 29*(1), 67–102.Google Scholar - Rivers, R., & Evans, T. S. (2013). What makes a site important? Centrality, gateways and gravity. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 125–150). Oxford: Oxford University Press.Google Scholar - Rivers, R., & Evans, T. S. (2014). New approaches to Archaic Greek settlement structure.
*Nouvelles de l’archéologie, 135*, 21–28.Google Scholar - Rothman, M. (1987). Graph theory and the interpretation of regional survey data.
*Paléorient, 13*(2), 73–91. doi: 10.3406/paleo.1987.4430.Google Scholar - Santley, R. S. (1991). The structure of the Aztec transport network. In C. D. Trombold (Ed.),
*Ancient road networks and settlement hierarchies in the New World*(pp. 198–210). Cambridge: Cambridge University Press.Google Scholar - Schich, M., & Coscia, M. (2011). Exploring co-occurrence on a meso and global level using network analysis and rule mining. In
*Proceedings of the ninth workshop on mining and Learning with Graphs (MLG ’11)*. San Diego: ACM.Google Scholar - Schich, M., Hidalgo, C. A., Lehmann, S. J., & Park, J. (2009). The network of subject co-popularity in Classical Archaeology.
*Bollettino di Archeologia Online*, 49–57.Google Scholar - Scholnick, J. B., Munson, J. L., & Macri, M. J. (2013). Positioning power in a multi-relational framework: A social network analysis of Classic Maya political rhetoric. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 95–124). Oxford: Oxford University Press.Google Scholar - Schweizer, T., Hage, P., Harary, F., Houseman, M., Kent, S., & Wolfe, A. W. (1997). Embeddedness of ethnographic cases: a social networks perspective.
*Current Anthropology, 38*(5), 739–760.Google Scholar - Shuchat, A. (1984). Matrix and network models in archaeology.
*Mathematics Magazine, 57*(1), 3–14.Google Scholar - Sindbæk, S. M. (2007a). Networks and nodal points: the emergence of towns in Early Viking Age Scandinavia.
*Antiquity, 81*(311), 119–132.Google Scholar - Sindbæk, S. M. (2007b). The small world of the Vikings: Networks in Early Medieval communication and exchange.
*Norwegian Archaeological Review, 40*, 59–74.Google Scholar - Sindbæk, S. M. (2009). Open access, nodal points, and central places: maritime communication and locational principles for coastal sites in south Scandinavia, c. AD 400–1200.
*Estonian Journal of Archaeology, 13*(2), 96. doi: 10.3176/arch.2009.2.02.Google Scholar - Sindbæk, S. M. (2013). Broken links and black boxes: Material affiliations and contextual network synthesis in the Viking world. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 71–94). Oxford: Oxford University Press.Google Scholar - Stoner, J. (2014). “Extending the self” through material culture: private letters and personal relationships in second-century AD Egypt.
*Archaeological Review from Cambridge, 29*(1), 129–143.Google Scholar - Swanson, S. (2003). Documenting prehistoric communication networks: a case study in the Paquimé polity.
*American Antiquity, 68*(4), 753–767.Google Scholar - Terrell, J. E. (1976). Island biogeography and man in Melanesia.
*Archaeology and Physical Anthropology in Oceania, 11*(1), 1–17.Google Scholar - Terrell, J. E. (1977a). Geographic systems and human diversity in the North Solomons.
*World Archaeology, 9*(1), 62–81.Google Scholar - Terrell, J. E. (1977b). Human biogeography in the Solomon Islands.
*Fieldiana Anthropology, 68*(1), 1–47.Google Scholar - Terrell, J. E. (2010a). Language and material culture on the Sepik Coast of Papua New Guinea: using social network analysis to simulate, graph, identify, and analyze social and cultural boundaries between communities.
*The Journal of Island and Coastal Archaeology, 5*(1), 3–32.Google Scholar - Terrell, J. E. (2010b). Social network analysis of the genetic structure of Pacific islanders.
*Annals of Human Genetics, 74*(3), 211–232. doi: 10.1111/j.1469-1809.2010.00575.x.Google Scholar - Terrell, J. E. (2013). Social network analysis and the practice of history. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 17–42). Oxford: Oxford University Press.Google Scholar - Van der Leeuw, S. E. (2013). Archaeology, networks, information processing, and beyond. In C. Knappett (Ed.),
*Network analysis in archaeology: New approaches to regional interaction*(pp. 335–348). Oxford: Oxford University Press.Google Scholar - Van Oyen, A. (2014). Les acteurs-réseaux en archéologie: État de la question et perspectives futures.
*Nouvelles de l’Archéologie, 135*, 14–20.Google Scholar - Verhagen, P., Brughmans, T., Nuninger, L., & Bertoncello, F. (2013). The long and winding road: Combining least cost paths and network analysis techniques for settlement location analysis and predictive modelling. In
*Proceedings of computer applications and quantitative techniques in archaeology conference 2012, Southampton*(pp. 357–366). Amsterdam: Amsterdam University Press.Google Scholar - Wernke, S. A. (2012). Spatial network analysis of a terminal prehispanic and early colonial settlement in highland Peru.
*Journal of Archaeological Science, 39*(4), 1111–1122. doi: 10.1016/j.jas.2011.12.014.Google Scholar - Zubrow, E. B. W. (1990). Modelling and prediction with geographic information systems: a demographic example from prehistoric and historic New York. In K. M. S. Allen, S. W. Green, & E. B. W. Zubrow (Eds.),
*Interpreting space: GIS and archaeology*(pp. 307–318). New York: Taylor & Francis.Google Scholar