Counts-of-counts similarity for prediction and search in relational data


Defining appropriate distance functions is a crucial aspect of effective and efficient similarity-based prediction and retrieval. Relational data are especially challenging in this regard. By viewing relational data as multi-relational graphs, one can easily see that a distance between a pair of nodes can be defined in terms of a virtually unlimited class of features, including node attributes, attributes of node neighbors, structural aspects of the node neighborhood and arbitrary combinations of these properties. In this paper we propose a rich and flexible class of metrics on graph entities based on earth mover’s distance applied to a hierarchy of complex counts-of-counts statistics. We further propose an approximate version of the distance using sums of marginal earth mover’s distances. We show that the approximation is correct for many cases of practical interest and allows efficient nearest-neighbor retrieval when combined with a simple metric tree data structure. An experimental evaluation on two real-world scenarios highlights the flexibility of our framework for designing metrics representing different notions of similarity. Substantial improvements in similarity-based prediction are reported when compared to solutions based on state-of-the-art graph kernels.

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    Preliminary experiments showed that a plain summation indeed achieves poor performance on TETs where different branches have very different number of children.

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    Note that a classification accuracy of 99.9% corresponds to F1 = 99.9, far higher than the one we achieved in Jaeger et al. (2013) for the same task (on another data set) with discriminant function and nearest neighbor retrieval.

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Proposition 1

\(d_{\text {r-count}}\) is a scale-invariant pseudo-metric with values in [0, 1].


The minimum of two counts is a positive semi-definite kernel, called histogram intersection kernel (Barla et al. 2003). The normalization is called cosine normalization, and the result is also a kernel (Schölkopf and Smola 2002). Let us refer to this kernel as

$$\begin{aligned} k(h_1,h_2)=\frac{min (c(h_1),c(h_2))}{\sqrt{c(h_1)\cdot c(h_2)}}. \end{aligned}$$

A kernel induces a pseudo-metric

$$\begin{aligned} d(h_1,h_2)=\sqrt{k(h_1,h_1)+k(h_2,h_2)-2k(h_1,h_2)}. \end{aligned}$$

For the normalized histogram intesection kernel we have that \(0 \le k(h_1,h_2) \le 1\) and \(k(h_1,h_1)=k(h_2,h_2)=1\), thus \(d(h_1,h_2)=\sqrt{2-2k(h_1,h_2)}\). The count distance is obtained as \(d_{\textit{r-count}}(h_1,h_2)=\frac{1}{2}d(h_1,h_2)^2\), a simplified version of the distance which preserves its properties. Non-negativity and symmetry are trivially preserved. For triangle inequality \(d(h_1,h_3) \le d(h_1,h_2)+d(h_2,h_3)\) implies that \(\alpha d(h_1,h_3)^2 \le \alpha (d(h_1,h_2)+d(h_2,h_3))^2 \le \alpha d(h_1,h_2)^2+ \alpha d(h_2,h_3)^2\) for any \(\alpha > 0\). Finally, \(d_{\textit{r-count}}\) is a pseudo-metric because any two distinct histograms having same counts have zero distance.\(\square \)

Proposition 4

\(d_{{\textit{memd}}}\) is a pseudo-metric with \(d_{{\textit{memd}}}\le d_{{\textit{emd}}}\).


We recall and introduce the following notation: \(\bar{h}_1,\bar{h}_2\) are normalized D-dimensional histograms with N bins in each dimension. Histogram cells are indexed by index vectors \(\varvec{i},\varvec{j},\ldots \in N^D\). The kth component of the index vector \(\varvec{i}\) is denoted \(\varvec{i}(k)\).

For \(k=1,\ldots ,D\) we have that \(d_{{\textit{memd}}}^{\downarrow k}(\bar{h}_1,\bar{h}_2):=d_{{\textit{emd}}}(\bar{h}_1^{\downarrow k},\bar{h}_2^{\downarrow k})\)\((k=1,\ldots ,D)\) is a pseudo-metric on the D-dimensional histograms \(\bar{h}_1,\bar{h}_2\), because it is induced by the metric \(d_{{\textit{emd}}}\) under the non-injective mapping \(\bar{h}\mapsto \bar{h}^{\downarrow k}\). \(d_{{\textit{memd}}}\) therefore is a sum of pseudo-metrics, and therefore also a pseudo-metric.

We denote by \(EMD(\bar{h}_1,\bar{h}_2)\) the constrained optimization problem defining the earth mover’s distance, i.e., \(d_{{\textit{emd}}}(\bar{h}_1,\bar{h}_2)\) is the cost of the optimal solution of \(EMD(\bar{h}_1,\bar{h}_2)\). A feasible solution for \(EMD(\bar{h}_1,\bar{h}_2)\) is a given by \(\varvec{f}=(f_{\varvec{i},\varvec{j}})_{\varvec{i},\varvec{j}}\), where

$$\begin{aligned} \sum _{\varvec{i}} f_{\varvec{i},\varvec{j}}=\bar{h}_1(\varvec{j}),\ \sum _{\varvec{j}} f_{\varvec{i},\varvec{j}}=\bar{h}_2(\varvec{i}) \end{aligned}$$

The cost of a feasible solution is

$$\begin{aligned} cost (\varvec{f})=\sum _{\varvec{i},\varvec{j}}f_{\varvec{i},\varvec{j}} d(\varvec{i},\varvec{j}) \end{aligned}$$

where d is the underlying metric on histogram cells. In our case, d is the Manhattan distance. However, all we require for this proof is that d is additive in the sense that there exist metrics \(d^{(k)}\) on \(\{1,\ldots ,N\}\)\((k=1,\ldots ,D)\) such that

$$\begin{aligned} d(\varvec{i},\varvec{j})=\sum _{k=1}^D d^{(k)}(\varvec{i}(k),\varvec{j}(k)). \end{aligned}$$

In the case of Manhattan distance, \(d^{(k)}(\varvec{i}(k),\varvec{j}(k))=|\varvec{i}(k)-\varvec{j}(k)|\).

Let \(\varvec{f}\) be a feasible solution for \(EMD(\bar{h}_1,\bar{h}_2)\). For \(k=1,\ldots ,D\) we define the marginal solutions

$$\begin{aligned} f^{\downarrow k}_{i,j}:= \sum _{ \begin{array}{c} \varvec{i}: \varvec{i}(k)=i\\ {\varvec{j}: \varvec{j}(k)=j} \end{array}} f_{\varvec{i},\varvec{j}} \end{aligned}$$

Then \(\varvec{f}^{\downarrow k}=( f^{\downarrow k}_{i,j})\) is a feasible solution solution of \(EMD(\bar{h}_1^{\downarrow k},\bar{h}_2^{\downarrow k})\), and we have

$$\begin{aligned} \textit{cost}(\varvec{f})=\sum _{\varvec{i},\varvec{j}}\sum _{k=1}^D f_{\varvec{i},\varvec{j}} d^{(k)}(\varvec{i}(k),\varvec{j}(k)) =\sum _{k=1}^D \sum _{i,j=1}^N f^{\downarrow k}_{i,j}d^{(k)}(i,j) = \sum _{k=1}^D \textit{cost}(\varvec{f}^{\downarrow k}) \end{aligned}$$

In particular, when \(\varvec{f}\) is a minimal cost solution of \(EMD(\bar{h}_1,\bar{h}_2)\), then we have \(d_{{\textit{emd}}}(\bar{h}_1,\bar{h}_2)=cost (\varvec{f})\), and

$$\begin{aligned} \sum _{k=1}^D \textit{cost}(\varvec{f}^{\downarrow k})\ge \sum _{k=1}^D d_{{\textit{emd}}}(\bar{h}_1^{\downarrow k},\bar{h}_2^{\downarrow k}) =d_{{\textit{memd}}}(\bar{h}_1,\bar{h}_2) \end{aligned}$$

\(\square \)

Proposition 5

If \(\bar{h}_1,\bar{h}_2\) are product histograms, then \(d_{{\textit{memd}}}(\bar{h}_1,\bar{h}_2 ) = d_{{\textit{emd}}}(\bar{h}_1,\bar{h}_2 )\).


Let \(\varvec{f}^{(k)}\) be feasible solutions for \(EMD(\bar{h}_1^{\downarrow k},\bar{h}_2^{\downarrow k})\) (\(k=1,\ldots ,D\)). Define

$$\begin{aligned} f_{\varvec{i},\varvec{j}}:= \prod _{k=1}^D f^{(k)}_{\varvec{i}(k),\varvec{j}(k)}. \end{aligned}$$

Then \(\varvec{f}=(f_{\varvec{i},\varvec{j}})\) is a feasible solution for \(EMD(\bar{h}_1,\bar{h}_2)\):

$$\begin{aligned} \sum _{\varvec{i}}f_{\varvec{i},\varvec{j}} = \sum _{\varvec{i}} \prod _k f^{(k)}_{\varvec{i}(k),\varvec{j}(k)} = \prod _k \sum _{i=1}^N f^{(k)}_{i,\varvec{j}(k)} = \prod _k \bar{h}_2^{\downarrow k}(\varvec{j}(k)) = \bar{h}_2(\varvec{j}), \end{aligned}$$

and similarly \(\sum _{\varvec{j}}f_{\varvec{i},\varvec{j}}=\bar{h}_1(\varvec{i})\). For the cost of the solutions we obtain:

$$\begin{aligned} cost (\varvec{f})= & {} \sum _{\varvec{i},\varvec{j}} \left( \sum _{k} d^{(k)}(\varvec{i}(k),\varvec{j}(k))\right) \prod _k f^{(k)}_{\varvec{i}(k),\varvec{j}(k)}\\= & {} \sum _{k} \sum _{\varvec{i},\varvec{j}} d^{(k)}(\varvec{i}(k),\varvec{j}(k)) \prod _k f^{(k)}_{\varvec{i}(k),\varvec{j}(k)} \\= & {} \sum _{k} \sum _{i,j=1}^N d^{(k)}(i,j) \sum _{\begin{array}{c} \varvec{i}: \varvec{i}(k)=i\\ {\varvec{j}: \varvec{j}(k)=j} \end{array}} \prod _k f^{(k)}_{\varvec{i}(k),\varvec{j}(k)}\\= & {} \sum _{k} \sum _{i,j=1}^N d^{(k)}(i,j) \sum _{i,j}f^{(k)}_{i,j} = \sum _{k} \textit{cost}(\varvec{f}^{(k)}). \end{aligned}$$

This implies \(d_{{\textit{emd}}}(\bar{h}_1,\bar{h}_2)\le \sum _k d_{{\textit{emd}}}(\bar{h}_1^{\downarrow k},\bar{h}_2^{\downarrow k})\), which together with Proposition 4 proves the proposition.\(\square \)


Proposition 6

\(d_{\textit{c-memd}}\) on node histogram trees is conditionally negative definite.


Let us recall the definition of \(d_{\textit{c-memd}}\) on node histogram trees and the definition of all its components:

$$\begin{aligned} d_{\textit{c-memd}}(H_1,H_2):= & {} \sum _{i=1}^k \frac{\gamma ^{d_i}}{s_i} d_{\textit{c-memd}}(h_{1,i},h_{2,i}) \end{aligned}$$
$$\begin{aligned} d_{\textit{c-memd}}(h_1,h_2):= & {} \frac{1}{2}(d_{\textit{r-count}}(h_1,h_2) + d_{{\textit{memd}}}(\bar{h}_1,\bar{h}_2)) \end{aligned}$$
$$\begin{aligned} d_{{\textit{memd}}}(\bar{h}_1,\bar{h}_2):= & {} \sum _{k=1}^D d_{{\textit{emd}}}(\bar{h}_1^{\downarrow k}, \bar{h}_2^{\downarrow k}) \end{aligned}$$
$$\begin{aligned} d_{{\textit{emd}}}(\bar{h}_1,\bar{h}_2):= & {} \sum _{k=1}^N |f_{1}(k)-f_{2}(k)| \end{aligned}$$
$$\begin{aligned} d_{\textit{r-count}}(h_1,h_2):= & {} 1-\frac{\textit{min}(c(h_1),c(h_2))}{\sqrt{c(h_1)\cdot c(h_2)}} \end{aligned}$$

Let us prove the statement in a bottom-up fashion:

  • \(d_{\textit{r-count}}(h_1,h_2)\) (Eq. 15) is conditionally negative definite, as \(\frac{min (c(h_1),c(h_2))}{\sqrt{c(h_1)\cdot c(h_2)}}\) is positive semi-definite (see proof of proposition 1), the negation of a p.s.d. function is conditionally negative definite (Berg et al. 1984), and summing a constant value does not change conditional negative definiteness.

  • \(d_{{\textit{emd}}}(\bar{h}_1,\bar{h}_2)\) (Eq. 14) is a Manhattan distance and thus it is conditionally negative definite (the same holds for other distances like the Euclidean one, see (Richards 1985) for a classical proof).

It follows that \(d_{\textit{c-memd}}(H_1,H_2)\) is conditionally negative definite, as it is a positively weighted sum of conditionally negative definite functions, and the property is closed under summation and multiplication by positive scalar. \(\square \)

Procedures for metric tree building and retrieval

In the following we briefly review the procedures for building and searching MTs, mostly following (Uhlmann 1991).

A MT is built from a dataset of node histogram trees, by recursively splitting data until a stopping condition is met. Algorithm 1 describes the procedure for building the MT. The algorithm has two parameters, the maximal tree depth (\(d_{max}\)) and the maximal bucket size (\(n_{max}\)) and two additional arguments, the current depth (initialized at \(d=1\)) and the data to be stored (data), represented as a set of node histogram trees, one for each entity. A MT is made of two types of nodes, internal ones and leaves. An internal node contains two entities and two branches. A leaf node contains a set of entities (the bucket). The MT construction proceeds by splitting data and recursively calling the procedure over each of the subsets, until a stopping condition is met. If the maximal tree depth is reached, or the current set to be splitted is not larger than the maximal bucket size, a leaf node is returned. If the stopping condition is not met, two entities z1 and z2 are chosen at random from the set of data (making sure they have a non-zero distance), and data are splitted according to their distances to these entities. Data that are closer to z1 go to the left branch, the others go to the right one, and the procedure recurses over each of the branches in turn.


Once the MT has been built, the fastest solution for approximate k-nearest-neighbor retrieval for a query instance H amounts to traversing the tree, following at each node the branch whose corresponding entity is closer to the query one, until a leaf node is found. The entities in the bucket contained in the leaf node are then sorted according to their distance to the query entity, and the k nearest neighbors are returned. See Algorithm 2 for the pseudocode of the procedure. Notice that this is a greedy approximate solution, as exact search would require to backtrack over alternative branches, pruning a branch when it cannot contain entities closer to the query than the current \(k\mathrm{th}\) neighbor (see Liu et al. 2005) for the details). Here we trade effectiveness for efficiency as our goal is to quickly find high quality solutions rather than discovering the actual nearest neighbors. Alternative solutions can be implemented in the latter case (Liu et al. 2005; Muja and Lowe 2014).

Both algorithms have as additional implicit parameter the distance function over NHTs, which can be the exact EMD-based NHT metric or its approximate version based on marginal EMD (exact for product histograms, see Proposition 5). Notice that for large databases, explicitly storing the NHT representation of each entity in the leaf buckets can be infeasible. In this case buckets only containt entity identifiers, and the corresponding NHTs are computed on-the-fly when scanning the bucket for the nearest neighbors. Standard caching solutions can be implemented to speed up this step.

Details on actor retrieval results

See Fig. 11.

Fig. 11

Color keys to actor feature graphs (Color figure online)

Test actor NN genre NN business
Muhammad I Ali John III Kerry Justin Ferrari
Kevin I Bacon Lance E. Nichols Charlie Sheen
Christian Bale Channing Tatum Hugh I Grant
Warren I Beatty Art I Howard Christopher Reeve
Humphrey Bogart Eddie I Graham Tony I Curtis
David I Bowie Ethan I Phillips Adam I Baldwin
Adrien Brody Mark I Camacho Kevin I Kline
Steve Buscemi Vincent I Price Keith I David
Michael I Caine Robert De Niro Robert De Niro
David Carradine Clint Howard Rutger Hauer
Jim Carrey Jason I Alexander Jake Gyllenhaal
Vincent Cassel Keith Szarabajka Dougray Scott
James I Coburn Ned Beatty Louis Gossett Jr.
Robbie Coltrane Rene Auberjonois H.B. Warner
Sean Connery Gene Hackman Paul I Newman
Kirk I Douglas Eli Wallach Burt Lancaster
Rupert Everett Brian Blessed Omar Sharif
Henry Fonda Dick I Curtis James I Mason
John I Goodman Christopher I Plummer Ron I Perlman
Al I Gore Jeroen Willems Dwight D. Eisenhower
Dustin Hoffman Rip Torn Pierce Brosnan
Stan Laurel Billy Franey Oliver Hardy
Jude Law Michael I Sheen Omar Sharif
Jack Lemmon Charles Dorety William I Holden
John Malkovich William H. Macy Mickey Rourke
Marcello Mastroianni James I Payne Ajay Devgn
Malcolm I McDowell Clint Howard Martin Sheen
Alfred Molina William H. Macy George I Kennedy
David I Niven Ivan F. Simpson William I Powell
Philippe Noiret Dominique Zardi Pat I O’Brien
Al I Pacino Jeremy Piven Tom Cruise
Chazz Palminteri Bobby Cannavale Norman Reedus
Gregory Peck James Seay Christopher I Lambert
Sean I Penn Andy I Garcia Michael I Douglas
Anthony I Perkins Nicholas I Campbell George C. Scott
Joe Pesci Stephen Marcus Anton Yelchin
Elvis Presley Berton Churchill Lee I Marvin
Robert I Redford Roscoe Ates Michael Keaton
Keanu Reeves Kevin I Pollak Antonio Banderas
Geoffrey Rush Jim I Carter Ian I McShane
Steven Seagal Frank Pesce Marlon Brando
Joseph Stalin Jimmy I Carter Tom I Herbert
Sylvester Stallone Nicolas Cage Johnny Depp
Ben Stiller Bill I Murray Antonio Banderas
David Suchet Danny Nucci James I Nesbitt
John Turturro Danny DeVito Bruce I Dern
Lee Van Cleef Robert I Peters Jack Warden
Christoph Waltz Frank I Gorshin Demin Bichir
Denzel Washington Michael V Shannon Tom Cruise
Orson Welles Donald Pleasence Rod Steiger

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Jaeger, M., Lippi, M., Pellegrini, G. et al. Counts-of-counts similarity for prediction and search in relational data. Data Min Knowl Disc 33, 1254–1297 (2019).

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  • Relational data mining
  • Graph mininga
  • Similarity search
  • Earth-mover’s distance
  • Statistical relational learning