Featurerich networks: going beyond complex network topologies
Abstract
The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex realworld relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Locationaware Networks, Knowledge Networks, Probabilistic Networks, and many other taskdriven and datadriven models. In this paper, we propose an overview of these models and their main applications, described under the common denomination of Featurerich Networks, i. e. models where the expressive power of the network topology is enhanced by exposing one or more peculiar features. The aim is also to sketch a scenario that can inspire the design of novel featurerich network models, which in turn can support innovative methods able to exploit the full potential of mining complex network structures in domainspecific applications.
Introduction
Structures built upon great quantities of networked entities, such as computer networks and social networks, have an undeniable central role in our everyday life. The need to study these complex realworld topologies, together with the growing ability to carry out these studies thanks to technological advances, recently made the use of complex network models pervasive in many disciplines such as computer science, physics, social science, as well as in interdisciplinary research environments.
Nowadays, it is straightforward to experience the use of complex networked data, thanks to the fact that collecting multirelational data from the Web is generally a simple and inexpensive task. Just think about the quantity of online social media platforms, crowdsourced data, online knowledge bases, and so on, that can be collected and studied with relatively low effort.
Nevertheless, besides relational data that can be modeled in a network topology, it is easy to recognize a quantity of “extra” features which serve as an inestimable source of information, that can be conveniently embedded in a network, thus enhancing the expressive power of the topology itself. Examples are given by temporal aspects of the data, quantitative and/or qualitative properties of the nodes, different relations between a common set of entities and different existence probabilities.

Attributed graphs, e. g. networks enclosing (vectors of) generic attributes on nodes and edges (“Attributed graphs” section);

Heterogeneous information networks, e. g. networks modeling heterogeneous node and edge types (“Heterogeneous information networks” section);

Multilayer networks, e. g. representing different online/offline relations between the same set of users (“Multilayer networks” section);

Temporal networks, e. g. modeling discrete/continuous time aspects in networked data (“Temporal networks” section);

Locationaware Networks, e. g. useful for the definition of recommender system (RecSys) applications like itinerary routing and points of interest (PoIs) planning (“Locationaware networks” section);

Probabilistic networks, e. g. networks modeling uncertain relations, such as sensor networks, or networks inferred from survey data (“Probabilistic networks” section).
Please note that the definition of featurerich network has been kept intentionally wide and flexible, with the aim to gather under a common denomination a series of network models exhibiting different structures and that were introduced for different needs, but that at the same time show some common characteristics and can lead to similar problems. For the same reason, the overview is not meant to be exhaustive, and other network models may exist which can be referred to as featurerich ones.
In this paper, we will provide an insight in the current status of research in featurerich network analysis and mining, describing the main types of featurerich networks and related applications. The aim is to show how embedding features in complex network models can make it possible to improve solutions to classic tasks (e. g. centrality, community detection, link prediction, information diffusion, and so on) and to focus on domains and research questions that have not been deeply investigated so far.
Attributed graphs
Together with the relational information (i.e., the graph), many data sources may also provide attributes describing the relationships or the entities of the network leading to the notions of a nodeattributed graph or an edgeattributed graph, respectively. When the attributes are associated with the relationships, the network can be represented by a weighted graph where the weights, usually used to measure the strength of the tie between the corresponding nodes, are replaced by a vector whose components correspond to attributes characterizing their relation. For instance, in a coauthorship network, the link between two coauthors can be described not only by the total number of their copublications but also by their dates or by the number for each copublications subtype (e. g. conference, journal, etc.). So, a vector can be assigned to the edges to take into account these attributes. Note that in specific cases, alternative network models may be used, such as temporal networks (cf. “Temporal networks” section) for modeling interactions over time or multiplex networks (cf. “Multilayer networks” section) for modeling each attribute by a specific relationship. The concept of (node) attributed networks refers rather to the case where attributes are assigned to the nodes for describing the corresponding entities. In a friendship network, e. g. the actors can be described by their genre and their age.
In literature, different definitions have been introduced. A first model has been defined by Zhou et al. (2009), an alternative by Yin et al. (2010):
Definition 1
(Attributed Network  Zhou et al. (2009)) An attributed network is defined as a graph G = (V, E) where V and E denote sets of nodes and edges; each node v∈V is associated with a is associated with a vector of attributes (v_{j},j∈{1,.. p})
Definition 2

a graph G = (V, E) describing the relationships between the entities, and

a bipartite graph G_{a}=(V∪V_{a},E_{a}) describing the relationships between the entities and the attributes in such a way that each node v from V is connected to attributenodes from V_{a}.
The choice of one of these models depends on the type and the number of the features retained to describe the entities of the network: The second definition is more appropriate when few categorical attributes are considered.
In different tasks, taking into account the attributes in addition to the relational information allows to improve the performance of the methods. Thus, attributed networks have been used with success for link prediction, inferring attributes or community detection (Zhou et al. 2010; Yang et al. 2013; Gong et al. 2014; Combe et al. 2015; Atzmueller et al. 2016). However, it is necessary to be careful because structure and attributes may disagree (Peel et al. 2017). Nevertheless, due to the homophily effect and to social influence, they are likely to be aligned, e. g. (McPherson et al. 2001; La Fond and Neville 2010; Mitzlaff et al. 2013; Mitzlaff et al. 2014; Atzmueller and Lemmerich 2018). Consequently, one can hope to benefit from the two sources, notably when one is missing or noisy. Finally it should be mentioned that generators have been recently designed to automatically build attributed networks (Akoglu and Faloutsos 2009; Palla et al. 2012; Kim and Leskovec 2012; Largeron et al. 2017). Such benchmarks are particularly useful for evaluating the performance of algorithms able to handle the two kinds of data.
A well known subcategory of attributed graphs includes the models used for direct organization and modeling of knowledge elements, e. g. given by concepts, their properties and (inter)relations. Rooted in the theory on semantic networks (Sowa 2006), such models are known as knowledge networks or knowledge graphs (Bizer et al. 2009; Hoffart et al. 2013). In such network structures, data is integrated into a comprehensive knowledge model capturing the relations between concepts and their properties in an explicit way, cf. (Bizer et al. 2009; Hoffart et al. 2013; Ristoski and Paulheim 2016). For instance, entities (concepts) are usually represented as nodes, there can be categories (labels) associated to node, and conceptual relations are given by directed edges between the nodes (Pujara et al. 2013). Following Paulheim (2017), from the point of construction, a knowledge network then mainly describes real world entities and their interrelations. The possible classes and relations can then also be potentially interrelated in an arbitrary way. Knowledge networks can be exploited in many ways, for example, in order to facilitate modeling, mining, inference, and reasoning. Then, tasks that are supported by knowledge networks include, for example, advanced feature engineering, e. g. (Atzmueller and Sternberg 2017; Wilcke et al. 2017). Furthermore, the constructed knowledge graph can serve as a data integration and exploration mechanism, such that the considered relations and additional information about the contained entities can be utilized by advanced graph mining methods, that work on such featurerich networks, e. g. by mining the respective attributed graph, e. g. (Atzmueller et al. 2016; Atzmueller et al. 2017). Knowledge graphs thus have a broad range of applications, ranging from knowledge modeling and structuring, cf. (Bizer et al. 2009; Hoffart et al. 2013) to advanced graph mining applications in diverse domains (Ristoski and Paulheim 2016; Wilcke et al. 2017; Atzmueller et al. 2016; Atzmueller and Sternberg 2017).
Heterogeneous information networks
The definition of Heterogeneous Information Network (HIN) models rises from the observation that sophisticated realworld networks can hardly be represented with standard network topologies. Most of realworld connections happen between entities that can be considered as different kinds, and describe different types of relations. A practical example is given by a bibliographic information network, containing entities of type paper, venue and author, where different relation types can connect nodes of different entity types (e. g. authorship between author and paper, publication between paper and venue, and so on) or even nodes of the same type (e. g. coauthorship between authors, citation between papers).
While HINs are a powerful tool to model realworld situations, on the other side the modeling process should be carried out by looking for a good tradeoff between homogeneous networks (i. e. all nodes of the same type) and complete heterogeneity (i. e. each node establishes a different entity type), since both extremes would result in a loss of information. For this reason, the authors in Sun and Han (2012) propose a typed, semistructured heterogeneous network model, defined as follows:
Definition 3
(Heterogeneous Information Network) An information network is defined as a directed graph \(G = (\mathcal {V}, \mathcal {E})\) with an object type mapping function \(\tau : \mathcal {V} \rightarrow \mathcal {A}\) and a link type mapping function \(\phi : \mathcal {E} \rightarrow \mathcal {R}\), where each object \(v \in \mathcal {V}\) belongs to one particular object type \(\tau (v) \in \mathcal {A}\), each link \(e \in \mathcal {E}\) belongs to a particular relation \(\phi (e) \in \mathcal {R}\), and if two links belong to the same relation type, the two links share the same starting object type as well as the ending object type. When the types of objects \(\mathcal {A} > 1\) or the types of relations \(\mathcal {R} > 1\), the network is called heterogeneous information network; otherwise, it is a homogeneous information network.
Given a complex heterogeneous information network, it is necessary to provide its meta level (i. e. schemalevel) description for better understanding the object types and link types in the network. Therefore, the concept of network schema is proposed, in order to describe the meta structure of a network (Sun and Han 2012):
Definition 4
(Network Schema) The network schema, denoted as \(T_{G} = (\mathcal {A},\mathcal {R})\), is a meta template for a heterogeneous network \(G = (\mathcal {V}, \mathcal {E})\) with the object type mapping \(\tau : \mathcal {V} \rightarrow \mathcal {A}\) and the link mapping \(\phi : \mathcal {E} \rightarrow \mathcal {R}\), which is a directed graph defined over object types \(\mathcal {A}\), with edges as relations from \(\mathcal {R}\).
The network schema of a heterogeneous information network has specified type constraints on the sets of objects and relationships between the objects. These constraints make a heterogeneous information network semistructured, guiding the exploration of the semantics of the network (Sun and Han 2012). This HIN model has been successfully used for several mining tasks, such us rankingbased clustering combinations (Sun et al. 2009; Sun et al. 2009), transductive and rankingbased classification (Ji et al. 2010; Ji et al. 2011), similarity search (Sun et al. 2011) and relationship prediction (Sun et al. 2012; Deng et al. 2014), and, more recently, learning of objectevent embeddings (Gui et al. 2017) and named entity linking (Shen et al. 2018). However, the notion of HIN is general enough to include other network models which are inherently heterogeneous in node and relation types, e. g. networks related to the InternetofThings (George and Thampi 2018; Misra et al. 2012; Qiu et al. 2016).
Multilayer networks
Multilayer network models provide a powerful and realistic tool for the analysis of complex realworld network systems, enabling an indepth understanding of the characteristics and dynamics of multiple, interconnected types of node relations and interactions (Dickison et al. 2016). While they can be seen as a form of HIN (cf. “Heterogeneous information networks” section), the main idea here is to model the different relations which may occur between the same set of entities in different layers. The layers can be seen as different interaction contexts, while the participation of an entity to different layers can be seen as a set of different instances of the same entity. When the only interlayer edges (i. e. edges linking instances in different layers) are the coupling edges (i. e. edges linking different instances of the same entity), this model is generally referred to as Multiplex Network. As a practical example, in social computing, an individual often has multiple accounts across different social networks. Multilayer networks can be easily used to link distributed user profiles belonging to the same user from multiple platforms, thus enabling the definition of advanced mining tasks, e. g. multilayer community detection (Kim and Lee 2015; Loe and Jensen 2015). Similarly, different layers can be used to model online and offline relations of different types happening in a social network (Gaito et al. 2012; Dunbar et al. 2015), such as followship, like/comment interactions, working relationship, lunch relationship, etc. A multilayer network model which has become very popular in literature is that proposed by Kivela et al. (2014):
Definition 5
(Multilayer Network) Let \(\mathcal {L} = \{L_{1}, \ldots, L_{\ell }\}\) be a set of layers and \(\mathcal {V}\) be a set of entities. We denote with \(V_{\mathcal {L}} \subseteq \mathcal {V} \times \mathcal {L}\) the set containing the entitylayer combinations in which an entity is present in the corresponding layer. The set \({E_{\mathcal {L}} \subseteq V_{\mathcal {L}} \times V_{\mathcal {L}}}\) contains the undirected links between such entitylayer pairs. We hence denote with \(G_{\mathcal {L}} = (V_{\mathcal {L}}, E_{\mathcal {L}}, \mathcal {V}, \mathcal {L})\) the multilayer network graph with set of nodes \(\mathcal {V}\).
Another multilayer network model, specifically conceived to represent multilayer social networks, is proposed by Magnani and Rossi in Dickison et al. (2016):
Definition 6
(Multilayer Social Network) Given a set of actors \(\mathcal {A}\) and a set of layers \(\mathcal {L}\), a multilayer network is defined as a quadruple \(G = (\mathcal {A}, \mathcal {L}, V, E)\) where (V,E) is a graph, \(V \subseteq \mathcal {A} \times \mathcal {L}\) and E⊆V×V.
In this model the concept of an Actor is a model upon the physical user, while the Nodes can be seen as the “instances” of the actor/user in different contexts/layers (e. g. accounts on different online social networks, or participation in different offline social networks).
Beyond the social networks domain (Dickison et al. 2016; Perna et al. 2018), multilayer networks have been successfully used to model relations and address mining tasks in different domains, such as airline companies (Cardillo et al. 2013), proteinprotein interactions (Bonchi et al. 2014), offline – online networks (Scholz et al. 2013), bibliographic networks (Boden et al. 2012), communication networks (Kim and Lee 2015; Bourqui et al2016), and remote sensing data (Interdonato et al. 2017).
Temporal networks
Real world phenomena are dynamic by nature, i. e. entities participating in a phenomenon and the interactions between them evolve over time, and each interaction typically happens at a specific time and lasts for a certain duration. Temporal networks (Li et al. 2017;Zignani et al. 2014) are the model used to represent these dynamic features in network graphs. Temporal networks have been referred to with different other terms, such as evolving graphs, timevarying graphs, timestamped graphs, dynamic networks, and so on.
Holme and Saramaki (2012) identify two main classes of temporal network, namely contact sequences and interval graphs. A contact sequence network is suitable for cases where there’s a set of entities V interacting with each other at certain times, and the durations of the interactions are negligible. Typical systems suitable to be represented as a contact sequence include communication data (sets of emails, phone calls, text messages, etc.), and physical proximity data where the duration of the contact is less important (e.g. sexual networks) (Holme and Saramäki 2012). A contact sequence network can be defined as follows:
Definition 7
(Contact sequence network) A contact sequence network G=(V,C) is defined by a set of vertices V with an associated set of contacts C, where each contact c∈C is a triple (i,j,t) where i,j∈V and t is a timestamp denoting a time of contact between i and j. A contact sequence network can be equivalently defined as G=(V,E,T,f), where E is a set of edges, T is a set of nonempty timestamp lists, and f:E→T is a function associating each edge to its timestamp list such that for each e∈E exists f(e)=T_{e}={t_{1},...,t_{n}}.
If the duration of the interactions is considered (i. e. each edge is active at certain time intervals), then the interval graph model is more suitable:
Definition 8
(Interval graph) An interval graph G=(V,E,T,f) is defined by a graph G=(V,E), a set of lists of time intervals T, and a function f:E→T associating a list of time intervals to each edge e∈E, such that T_{e}={(t_{1},t1′),...,(t_{n},tn′)}, with each couple (t_{i},ti′) denoting the beginning and ending time of a time interval.
Examples of systems that are natural to model as interval graphs include proximity networks (where a contact can represent that two individuals have been close to each other for some extent of time), seasonal food webs where a time interval represents that one species is the main food source of another at some time of the year, and infrastructural systems like the Internet (Holme and Saramäki 2012). In both cases (i. e. starting from a contact sequence network or from an interval graph), a static time aggregated graph can be derived, where an edge between two nodes i and j exists if and only if there is at least a contact between i and j. Temporal networks have been used to address problems in different domains, such as community detection in dynamic social networks (Rossetti et al. 2017), activity pattern analysis of editors (Yasseri et al. 2012), temporal aspects of protein interaction (Han et al. 2004) and generegulatory networks (Lèbre et al. 2010), analysis of temporal text networks (Vega and Magnani 2018), analysis of epidemic spreading (Moinet et al. 2018;Onaga et al. 2017) and problems related to mobile devices (Tang et al. 2011;Quadri et al. 2014), just to name a few.
Locationaware networks
As discussed for the time dimension (cf. “Temporal networks” section), in several cases modeling networks from realworld phenomena may require taking into account spatial features. The use of locationbased (e. g. georeferenced) information is commonly related to specific research fields, e. g. the ones connected to geographical issues and analyses. Nevertheless, in recent years the increasing availability of gpsequipped mobile devices gave rise to the development of locationbased social networking (LBSN) services, such as Foursquare, Facebook Places, Google Latitude, Tripadvisor and Yelp. Consequently, several research approaches have been proposed which make use of geographical and spatiotemporal features in social network analysis problems.
Based on the analysis inBao et al. (2015), in typical cases different types of locationaware networks can be defined, depending on which informations are extracted from the LBSN:
Definition 9
(Locationlocation graph) A locationlocation graph G=(V,E) is a graph where nodes in V represent locations and directed edges in E⊆V×V represent the relation between two locations. The semantic of the relation can be defined in different ways, e.g. distance between the location (i. e. expressed as edge weight), similarity or visits by the same users.
Definition 10
(Userlocation graph) A userlocation graph G=(U,V,E) is a bipartite graph where nodes in U represent users, nodes in V represent locations and directed edges in E⊆U×V represent relations between users and locations. The semantic of the relation can be flexible, e.g. may indicate that a user visited or rated a certain location.
Definition 11
(Useruser graph) A useruser graph G=(V,E) is a graph where nodes in V represent users and directed edges in E⊆V×V represent relations between users. Some typical edge semantics here may be physical distances, friendship on a LBSN, or features derived from users’ location histories (e.g. edges may connect users having visited a common location).
Locationaware networks built upon LSBN data are generally used for PointofInterest (POI) recommendation tasks (Bao et al. 2015;Zhang and Chow 2015;Liu 2018), with the aim to combine geographical and social influence in the recommendation process. A locationbased Influence Maximization problem is addressed inZhou et al. (2015), exploiting LSBN to carry out product promotion in a Online to Offline (O2O) business model. A locationaware multilayer network is proposed inInterdonato and Tagarelli (2017), for a POI recommendation task, which integrates locationaware features from a LSBN (Foursquare), geographical features from Google Maps and conceptual features from Wikipedia on different layers.
Networks based on geographical features can also be extracted from remote sensing data, i. e. satellite images. An approached based on evolution graphs is proposed inGuttler et al. (2017), in order to detect spatiotemporal dynamics satellite image time series. Different evolution graphs are produced for particular areas within the study site, which store information about the temporal evolution of a specific geographical area. Then the graph are both studied separately and compared to each other in order to provide a global analysis on the dynamical evolution of the site.
Probabilistic networks
When using networks to model realworld complex phenomena, it is easy to incur in situations where the existence of the relationship between two entities is uncertain. The sources of this uncertainty can be manifold, e. g. links may be derived from erroneous or noisy measurements, inferred from probabilistic models (Monti and Boldi 2017), or even intentionally obfuscated for various reasons. A practical example is offered by biological networks representing protein and gene interactions. Since the interactions are observed through noisy and errorprone experiments, link existence is uncertain, and a major part of uncertainty may arise in social networks for reasons related to data collection (e. g. data collected through automated sensors, inferred from anonymized communication data or from selfreporting/logging data (Adar and Ré 2007)), or because the network structure is based on prediction algorithms (e. g. approaches based on link prediction (LibenNowell and Kleinberg 2007)), or simply because actual interactions in online and offline social networks are difficult to measure. Similar issues may happen when coping with Temporal (cf. “Temporal networks” section) and Locationaware (cf. “Locationaware networks” section) networks, always due to data collection (von Landesberger et al. 2017;Wunderlich et al. 2017). In specific cases, uncertainty in the link structure may also be intentionally injected in a network for privacy reasons (Boldi et al. 2012).
All these situations can be handled by using probabilistic network models, often referred to as uncertain graphs, whose edges are labeled with a probability of existence. This probability represents the confidence with which one believes that the relation corresponding to the edge holds in reality (Parchas et al. 2015). A typical probabilistic network, referred to as Uncertain Graph, is defined inParchas et al. (2015):
Definition 12
(Uncertain Graph) An uncertain graph is defined as a triple \(\mathcal {G}=(V,E,p)\), where function p:E→(0,1] assigns a probability of existence to each edge.
Nevertheless, the expressive power enabled by a probabilistic network schema naturally carries with it an explosion in complexity, e. g. the exponential number of possible worlds may even prevent exact query evaluation on the graph. More specifically, even simple queries on deterministic graphs become #Pcomplete problems on uncertain graphs, and also approximated approaches based on sampling may be too expensive in most cases. To overcome these issues, Parchas et al. propose to create deterministic representative instances of uncertain graphs that maintain the underlying graph properties (Parchas et al. 2015).
Conclusions and future challenges
Table summarizing the main features exposed for nodes and edges for the discussed featurerich network models
Network model  Node features  Edge features 

Attributed graph  Attributes vector  Attributes vector 
Heterogeneous information network  Object type  Relation type 
Multilayer network  Layer  Layer 
Temporal network    Timestamp/Time interval 
Locationaware network  Geolocation  Distance/Visiting/Rating 
Probabilistic network    Probability of existence 
Notes
Acknowledgements
Not applicable.
Funding
Not applicable.
Availability of data and materials
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
Authors’ contributions
RI contributed to “Introduction”, “Heterogeneous information networks”, “Multilayer networks”, “Temporal networks”, “Locationaware networks”, “Conclusions and future challenges”, sections and supervised the writing of the article. MA contributed to sections “Introduction”, “Attributed graphs” and “Multilayer networks”. CL and RK contributed to “Attributed graphs” and “Multilayer networks” sections. SG and AS contributed to “Temporal networks”, “Locationaware networks” and “Probabilistic networks” sections. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
 Abiteboul, S, Kanellakis PC, Grahne G (1987) On the representation and querying of sets of possible worlds In: Proceedings of the Association for Computing Machinery Special Interest Group on Management of Data 1987 Annual Conference, San Francisco, CA, USA, May 2729, 1987, 34–48.Google Scholar
 Adar, E, Ré C (2007) Managing uncertainty in social networks. IEEE Data Eng Bull 30(2):15–22.Google Scholar
 Akoglu, L, Faloutsos C (2009) Rtg: a recursive realistic graph generator using random typing. Data Min Knowl Disc (DMKD) 19(2):194–209.MathSciNetCrossRefGoogle Scholar
 Atzmueller, M, Doerfel S, Mitzlaff F (2016) DescriptionOriented Community Detection using Exhaustive Subgroup Discovery. Inf Sci 329:965–984. Publisher: Elsevier, United States.CrossRefGoogle Scholar
 Atzmueller, M, Kloepper B, Mawla HA, Jäschke B, Hollender M, Graube M, Arnu D, Schmidt A, Heinze S, Schorer L, Kroll A, Stumme G, Urbas L (2016) Big Data Analytics for Proactive Industrial Decision Support: Approaches & First Experiences in the Context of the FEE Project. atp edition 58(9).Google Scholar
 Atzmueller, M, Lemmerich F (2018) Homophily at Academic Conferences In: Proc. WWW 2018 (Companion).. ACM Press, New York.Google Scholar
 Atzmueller, M, Schmidt A, Kloepper B, Arnu D (2017) HypGraphs: An Approach for Analysis and Assessment of GraphBased and Sequential Hypotheses In: New Frontiers in Mining Complex Patterns. Postproceedings NFMCP 2016, volume 10312 of LNAI.. Springer, Berlin/Heidelberg.Google Scholar
 Atzmueller, M, Sternberg E (2017) MixedInitiative Feature Engineering Using Knowledge Graphs In: Proc. 9th International Conference on Knowledge Capture (KCAP).. ACM Press, New York.Google Scholar
 Bao, J, Zheng Y, Wilkie D, Mokbel MF (2015) Recommendations in locationbased social networks: a survey. GeoInformatica 19(3):525–565.CrossRefGoogle Scholar
 Bizer, C, Lehmann J, Kobilarov G, Auer S, Becker C, Cyganiak R, Hellmann S (2009) Dbpediaa crystallization point for the web of data. Web Semant Sci Serv Agents World Wide Web 7(3):154–165.CrossRefGoogle Scholar
 Boden, B, Günnemann S, Hoffmann H, Seidl T (2012) Mining coherent subgraphs in multilayer graphs with edge labels In: Proc. ACM KDD, 1258–1266.. ACM Press, New York.Google Scholar
 Boldi, P, Bonchi F, Gionis A, Tassa T (2012) Injecting uncertainty in graphs for identity obfuscation. PVLDB 5(11):1376–1387.Google Scholar
 Bonchi, F, Gionis A, Gullo F, Ukkonen A (2014) Distance oracles in edgelabeled graphs In: Proc. EDBT, 547–558.Google Scholar
 Bourqui, R, Ienco D, Sallaberry A, Poncelet P (2016) Multilayer graph edge bundling In: Proc. PacificVis, 184–188.. IEEE Computer Society, Washington, D.C.Google Scholar
 Cardillo, A, GomezGardenes J, Zanin M, Romance M, Papo D, del Pozo F, Boccaletti S (2013) Emergence of network features from multiplexity. Sci Rep 3:1344.CrossRefGoogle Scholar
 Combe, D, Largeron C, Géry M, EgyedZsigmond E (2015) Ilouvain: An attributed graph clustering method In: Advances in Intelligent Data Analysis XIV  14th International Symposium, IDA 2015, Saint Etienne, France, October 2224, 2015, Proceedings, 181–192.. Springer, Berlin/Heidelberg.Google Scholar
 Dalvi, NN, Suciu D (2004) Efficient query evaluation on probabilistic databases In: (e)Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, August 31  September 3 2004, 864–875.. Morgan Kaufmann, Burlington.Google Scholar
 Deng, H, Han J, Li H, Ji H, Wang H, Lu Y (2014) Exploring and inferring useruser pseudofriendship for sentiment analysis with heterogeneous networks. Stat Anal Data Min 7(4):308–321.MathSciNetCrossRefGoogle Scholar
 Dickison, ME, Magnani M, Rossi L (2016) Multilayer social networks. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
 Dunbar, RIM, Arnaboldi V, Conti M, Passarella A (2015) The structure of online social networks mirrors those in the offline world. Soc Networks 43:39–47.CrossRefGoogle Scholar
 Gaito, S, Rossi GP, Zignani M (2012) Facencounter: Bridging the gap between offline and online social networks In: Eighth International Conference on Signal Image Technology and Internet Based Systems, SITIS 2012, Sorrento, Naples, Italy, November 2529, 2012, 768–775.. IEEE Computer Society, Washington, D.C.Google Scholar
 George, G, Thampi SM (2018) A graphbased security framework for securing industrial iot networks from vulnerability exploitations. IEEE Access 6:43586–43601.CrossRefGoogle Scholar
 Gong, NZ, Talwalkar A, Mackey L, Huang L, Shin ECR, Stefanov E, (Runting) Shi E, Song D (2014) Joint link prediction and attribute inference using a socialattribute network. ACM Trans Intell Syst Technol 5(2):27:1–27:20.CrossRefGoogle Scholar
 Gui, H, Liu J, Tao F, Jiang M, Norick B, Kaplan LM, Han J (2017) Embedding learning with events in heterogeneous information networks. IEEE Trans Knowl Data Eng 29(11):2428–2441.CrossRefGoogle Scholar
 Guttler, F, Ienco D, Nin J, Teisseire M, Poncelet P (2017) A graphbased approach to detect spatiotemporal dynamics in satellite image time series. ISPRS J Photogramm Remote Sens 130:92–107.ADSCrossRefGoogle Scholar
 Han, JDJ, Bertin N, Hao T, Goldberg DS, Berriz GF, Zhang LV, Dupuy D, Walhout AJM, Cusick ME, Roth FP, Vidal M (2004) Evidence for dynamically organized modularity in the yeast proteinprotein interaction network. Nature.Google Scholar
 Hoffart, J, Suchanek FM, Berberich K, Weikum G (2013) Yago2: A spatially and temporally enhanced knowledge base from wikipedia. Artif Intell 194:28–61.MathSciNetzbMATHCrossRefGoogle Scholar
 Holme, P, Saramäki J (2012) Temporal networks. Phys Rep 519(3):97–125.ADSCrossRefGoogle Scholar
 Interdonato, R, Tagarelli A (2017) Personalized recommendation of pointsofinterest based on multilayer local community detection In: Proc. Social Informatics  9th International Conference, SocInfo 2017, Oxford, UK, September 1315 2017, Proceedings, Part I, 552–571.. Springer, Berlin/Heidelberg.Google Scholar
 Interdonato, R, Tagarelli A, Ienco D, Sallaberry A, Poncelet P (2017) Local community detection in multilayer networks. Data Min Knowl Discov 31(5):1444–1479.MathSciNetCrossRefGoogle Scholar
 Ji, M, Han J, Danilevsky M (2011) Rankingbased classification of heterogeneous information networks In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 2124, 2011, 1298–1306.. ACM Press, New York.Google Scholar
 Ji, M, Sun Y, Danilevsky M, Han J, Gao J (2010) Graph regularized transductive classification on heterogeneous information networks In: Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 2024 2010, Proceedings, Part I, 570–586.. Springer, Berlin/Heidelberg.Google Scholar
 Jin, R, Liu L, Aggarwal CC (2011) Discovering highly reliable subgraphs in uncertain graphs In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 2124, 2011, 992–1000.. ACM Press, New York.Google Scholar
 Kim, J, Lee JG (2015) Community detection in multilayer graphs: A survey. SIGMOD Record 44(3):37–48.CrossRefGoogle Scholar
 Kim, M, Leskovec J (2012) Multiplicative attribute graph model of realworld networks. Internet Math 8(12):113–160.MathSciNetzbMATHCrossRefGoogle Scholar
 Kivela, M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Mutilayer networks. J Complex Networks 2(3):203–271.CrossRefGoogle Scholar
 La Fond, T, Neville J (2010) Randomization tests for distinguishing social influence and homophily effects In: Proceedings of the 19th international conference on World wide web, 601–610.. ACM, New York.CrossRefGoogle Scholar
 Largeron, C, Mougel PN, Benyahia O, Zaïane OR (2017) Dancer: dynamic attributed networks with community structure generation. Knowl Inf Syst 53(1):109–151.CrossRefGoogle Scholar
 Lèbre, S, Becq J, Devaux F, Stumpf MPH, Lelandais G (2010) Statistical inference of the timevarying structure of generegulation networks. BMC Syst Biol 4(1):130.CrossRefGoogle Scholar
 Li, A, Cornelius SP, Liu YY, Wang L, Barabási AL (2017) The fundamental advantages of temporal networks. Science 358(6366):1042–1046.ADSCrossRefGoogle Scholar
 LibenNowell, D, Kleinberg JM (2007) The linkprediction problem for social networks. JASIST 58(7):1019–1031.CrossRefGoogle Scholar
 Liu, S (2018) User modeling for pointofinterest recommendations in locationbased social networks: The state of the art. Mob Inf Syst 2018:7807461:1–7807461:13.Google Scholar
 Loe, CW, Jensen HJ (2015) Comparison of communities detection algorithms for multiplex. Physica A 431:29–45.ADSMathSciNetzbMATHCrossRefGoogle Scholar
 McPherson, M, SmithLovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annu Rev Sociol 27(1):415–444.CrossRefGoogle Scholar
 Misra, S, Barthwal R, Obaidat MS (2012) Community detection in an integrated internet of things and social network architecture In: 2012 IEEE Global Communications Conference (GLOBECOM), 1647–1652.. IEEE Computer Society, Washington, D.C.CrossRefGoogle Scholar
 Mitzlaff, F, Atzmueller M, Hotho A, Stumme, G (2014) The Social Distributional Hypothesis. J Soc Netw Anal Min 4(216):1–14.Google Scholar
 Mitzlaff, F, Atzmueller M, Stumme G, Hotho A (2013) Semantics of User Interaction in Social Media. In: Ghoshal G, PoncelaCasasnovas J, Tolksdorf R (eds)Complex Networks IV, volume 476 of Studies in Computational Intelligence.. Springer, Heidelberg.Google Scholar
 Moinet, A, PastorSatorras R, Barrat A (2018) Effect of risk perception on epidemic spreading in temporal networks. Phys Rev E 97:012313.ADSCrossRefGoogle Scholar
 Monti, C, Boldi P (2017) Estimating latent featurefeature interactions in large featurerich graphs. Internet Math:2017.Google Scholar
 Onaga, T, Gleeson JP, Masuda N (2017) Concurrencyinduced transitions in epidemic dynamics on temporal networks. Phys Rev Lett 119:108301.ADSCrossRefGoogle Scholar
 Palla, K, Knowles DA, Ghahramani Z (2012) An infinite latent attribute model for network data In: Proceedings of the 29th International Conference on Machine Learning (ICML), 1607–1614.. Omnipress, USA.Google Scholar
 Parchas, P, Gullo F, Papadias D, Bonchi F (2015) Uncertain graph processing through representative instances. ACM Trans Database Syst 40(3):20:1–20:39.MathSciNetCrossRefGoogle Scholar
 Paulheim, H (2017) Knowledge graph refinement: A survey of approaches and evaluation methods. Semant web 8(3):489–508.CrossRefGoogle Scholar
 Peel, L, Larremore DB, Clauset A (2017) The ground truth about metadata and community detection in networks. Sci Adv 3(5). American Association for the Advancement of Science.Google Scholar
 Perna, D, Interdonato R, Tagarelli A (2018) Identifying users with alternate behaviors of lurking and active participation in multilayer social networks. IEEE Trans Comput Soc Syst 5(1):46–63.CrossRefGoogle Scholar
 Potamias, M, Bonchi F, Gionis A, Kollios G (2010) knearest neighbors in uncertain graphs. PVLDB 3(1):997–1008.Google Scholar
 Pujara, J, Miao H, Getoor L, Cohen W (2013) Knowledge graph identification In: International Semantic Web Conference, 542–557.. Springer, Berlin/Heidelberg.Google Scholar
 Qiu, T, Luo D, Xia F, Deonauth N, Si W, Tolba A (2016) A greedy model with small world for improving the robustness of heterogeneous Internet of Things. Comput Netw 101:127–143.CrossRefGoogle Scholar
 Quadri, C, Zignani M, Capra L, Gaito S, Rossi GP (2014) Multidimensional human dynamics in mobile phone communications. PLoS ONE 9(7):1–12.CrossRefGoogle Scholar
 Ristoski, P, Paulheim H (2016) Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey. Web Semant 36:1–22.CrossRefGoogle Scholar
 Rossetti, G, Pappalardo L, Pedreschi D, Giannotti F (2017) Tiles: an online algorithm for community discovery in dynamic social networks. Mach Learn 106(8):1213–1241.MathSciNetCrossRefGoogle Scholar
 Scholz, C, Atzmueller M, Barrat A, Cattuto C, Stumme G (2013) New Insights and Methods For Predicting FaceToFace Contacts. In: Kiciman E, Ellison NB, Hogan B, Resnick P, Soboroff I (eds)Proc. 7th Intl. AAAI Conference on Weblogs and Social Media.. AAAI Press, Palo Alto.Google Scholar
 Shen, W, Han J, Wang J, Yuan X, Yang Z (2018) SHINE+: A general framework for domainspecific entity linking with heterogeneous information networks. IEEE Trans Knowl Data Eng 30(2):353–366.CrossRefGoogle Scholar
 Sowa, JF (2006) Semantic networks. Encycl Cogn Sci. https://doi.org/10.1002/0470018860.s00065.
 Sun, Y, Han J (2012) Mining Heterogeneous Information Networks: Principles and Methodologies. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool Publishers.Google Scholar
 Sun, Y, Han J, Zhao P, Yin Z, Cheng H, Wu T (2009) RankClus: integrating clustering with ranking for heterogeneous information network analysis In: Proc. Int. Conf. on Extending Database Technology (EDBT), 565–576.. ACM Press, New York.CrossRefGoogle Scholar
 Sun, Y, Yu Y, Han J (2009) Rankingbased clustering of heterogeneous information networks with star network schema In: Proc. ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), 797–806.. ACM Press, New York.CrossRefGoogle Scholar
 Sun, Y, Han J, Aggarwal CC, Chawla NV (2012) When will it happen?: relationship prediction in heterogeneous information networks In: Proceedings of the Fifth International Conference on Web Search and Web Data Mining, WSDM 2012, Seattle, WA, USA, February 812, 2012, 663–672.. ACM, New York.Google Scholar
 Sun, Y, Han J, Yan X, Yu PS, Wu T (2011) Pathsim: Meta pathbased topk similarity search in heterogeneous information networks. PVLDB 4(11):992–1003.Google Scholar
 Tang, JK, Mascolo C, Musolesi M, Latora V (2011) Exploiting temporal complex network metrics in mobile malware containment In: 12th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WOWMOM 2011, Lucca, Italy, 2024 June, 2011, 1–9.. IEEE Computer Society, Washington, D.C.Google Scholar
 Vega, D, Magnani M (2018) Foundations of temporal text networks. Appl Netw Sci 3(1):25:1–25:26.CrossRefGoogle Scholar
 von Landesberger, T, Bremm S, Wunderlich M (2017) Typology of uncertainty in static geolocated graphs for visualization. IEEE Comput Graph Appl 37(5):18–27.CrossRefGoogle Scholar
 Wilcke, X, Bloem P, de Boer V (2017) The Knowledge Graph as the Default Data Model for Learning on Heterogeneous Knowledge. Data Sci 1(12):39–57.Google Scholar
 Wunderlich, M, Ballweg K, Fuchs G, von Landesberger T (2017) Visualization of delay uncertainty and its impact on train trip planning: A design study. Comput Graph Forum 36(3):317–328.CrossRefGoogle Scholar
 Yang, J, McAuley J, Leskovec J (2013) Community Detection in Networks with Node Attributes In: 2013 IEEE 13th International Conference on Data Mining, 1151–1156.. IEEE Computer Society, Washington, D.C.CrossRefGoogle Scholar
 Yasseri, T, Sumi R, Kertész J (2012) Circadian patterns of wikipedia editorial activity: A demographic analysis. PLoS ONE 7:1–8.Google Scholar
 Yin, Z, Gupta M, Weninger T, Han J (2010) Linkrec: A unified framework for link recommendation with user attributes and graph structure In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, 1211–1212.. ACM Press, New York.CrossRefGoogle Scholar
 Zhang, JD, Chow CY (2015) Pointofinterest recommendations in locationbased social networks. SIGSPATIAL Special 7(3):26–33.CrossRefGoogle Scholar
 Zhou, T, Cao J, Liu B, Xu S, Zhu Z, Luo J (2015) Locationbased influence maximization in social networks In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19  23, 2015, 1211–1220.. ACM Press, New York.Google Scholar
 Zhou, Y, Cheng H, Yu JX (2009) Graph clustering based on structural/attribute similarities. Proc VLDB Endow 2(1):718–729.CrossRefGoogle Scholar
 Zhou, Y, Cheng H, Yu JX (2010) Clustering large attributed graphs: An efficient incremental approach In: 2010 IEEE International Conference on Data Mining, 689–698.. IEEE Computer Society, Washington, D.C.CrossRefGoogle Scholar
 Zignani, M, Gaito S, Rossi GP, Zhao X, Zheng H, Zhao BY (2014) Link and triadic closure delay: Temporal metrics for social network dynamics In: Proceedings of the Eighth International Conference on Weblogs and Social Media, ICWSM 2014, Ann Arbor, Michigan, USA, June 14, 2014.. The AAAI Press, USA.Google Scholar
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