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Non-Negative Matrix Factorizations for Multiplex Network Analysis

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Non-Negative Matrix Factorizations for Multiplex Network Analysis JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Non-Negative Matrix Factorizations for Multiplex Network Analysis Vladimir Gligorijevic,´ Yannis Panagakis, Member, IEEE and Stefanos Zafeiriou, Member, IEEE Abstract—Networks have been a general tool for representing, analyzing, and modeling relational data arising in several domains. One of the most important aspect of network analysis is community detection or network clustering. Until recently, the major focus have been on discovering community structure in single (i.e., monoplex) networks. However, with the advent of relational data with multiple modalities, multiplex networks, i.e., networks composed of multiple layers representing different aspects of relations, have emerged. Consequently, community detection in multiplex network, i.e., detecting clusters of nodes shared by all layers, has become a new challenge. In this paper, we propose Network Fusion for Composite Community Extraction (NF-CCE), a new class of algorithms, based on four different non–negative matrix factorization models, capable of extracting composite communities in multiplex networks. Each algorithm works in two steps: first, it finds a non–negative, low–dimensional feature representation of each network layer; then, it fuses the feature representation of layers into a common non–negative, low–dimensional feature representation via collective factorization. The composite clusters are extracted from the common feature representation. We demonstrate the superior performance of our algorithms over the state–of–the–art methods on various types of multiplex networks, including biological, social, economic, citation, phone communication, and brain multiplex networks. Index Terms—Multiplex networks, non-negative matrix factorization, community detection, network integration F 1 INTRODUCTION ETWORKS (or graphs1) along with their theoretical personal, professional, social, etc.). In biology, different ex- N foundations are powerful mathematical tools for rep- periments or measurements can provide different types of resenting, modeling, and analyzing complex systems arising interactions between genes. Reducing these networks to in several scientific disciplines including sociology, biol- a single type interactions by disregarding their multiple ogy, physics, and engineering among others [1]. Concretely, modalities is often a very crude approximation that fails social networks, economic networks, biological networks, to capture a rich and holistic complexity of the system. In telecommunications networks, etc. are just a few examples order to encompass a multimodal nature of these relations, of graphs in which a large set of entities (or agents) corre- a multiplex network representation has been proposed [14]. spond to nodes (or vertices) and relationships or interactions Multiplex networks (also known as multidimensional, mul- between entities correspond to edges (or links). Structural tiview or multilayer networks) have recently attracted a lot analysis of these networks have yielded important findings of attention in network science community. They can be in the corresponding fields [2], [3]. represented as a set of graph layers that share a common set Community detection (also known as graph clustering or of nodes, but different set of edges in each layer (cf. Fig. 1). module detection) is one of the foremost problems in network With the emergence of this network representation, finding analysis. It aims to find groups of nodes (i.e., clusters, composite communities across different layers of multiplex modules or communities) that are more densely connected to network has become a new challenge [14], [15]. each other than they are to the rest of the network [4]. Even Here, distinct from the previous approaches (reviewed arXiv:1612.00750v2 [cs.SI] 25 Jan 2017 thought the volume of research on community detection in Section 2), we focus on multiplex community detection. is large, e.g., [5], [6], [7], [8], [9], [10], [11], [12], [13], the Concretely, a novel and general model, namely the Network majority of these methods focus on networks with only one Fusion for Composite Community Extraction (NF-CCE) type of relations between nodes (i.e., networks of single type along with its algorithmic framework is developed in Sec- interaction). tion 3. The heart of the NF-CCE is the Collective Non- However, many real-world systems are naturally repre- negative Matrix Factorization (CNMF), which is employed sented with multiple types of relationships, or with relation- in order to collectively factorize adjacency matrices repre- ships that chance in time. Such systems include subsystems senting different layers in the network. The collective factor- or layers of connectivity representing different modes of ization facilitate us to obtain a consensus low-dimensional complexity. For instance, in social systems, users in social latent representation, shared across the decomposition, and networks engage in different types of interactions (e.g., hence to reveal the communities shared between the net- work layers. The contributions of the paper are as follows: • V. Gligorijevi´c,Y. Panagakis and S. Zafeiriou are with the Department of Computing, Imperial College London, UK. Corresponding author: V. 1) Inspired by recent advances in non-negative matrix Gligorijevi´c,email: [email protected] factorization (NMF) techniques for graph clustering 1. we use terms graphs and network interchangeably throughout this [16], [17] and by using tools for subspace analysis paper on Grassmann manifold [18], [19], [20], we propose JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 2 a general framework for extracting composite com- c1 c2 munities from multiplex networks. In particular, the framework NF-CCE, utilizes four different NMF G3(V,E3) techniques, each of which is generalized for collec- c tive factorization of adjacency matrices representing 1 c2 network layers, and u for computing a consensus G2(V,E2) low-dimensional latent feature matrix shared across the decomposition that is used for extracting latent c1 c2 communities common to all network layers. To this end, a general model involving factorization of net- G1(V,E1) works’ adjacency matrices and fusion of their low- dimensional subspace representation on Grassmann Fig. 1. An example of a multiplex network with 11 nodes present in manifold is proposed in Section 3. three complementary layers denoted in different colors. Two different 2) Unlike a few matrix factorization-based methods for communities across all three layers can be identified. multiplex community extraction that have been pro- posed so far, e.g., [20], [21], [22], [23], that directly decompose matrices representing network layers 2 BACKGROUND AND RELATED WORK into a low-dimensional representation common to 2.1 Single-layer (monoplex) networks all network layers, NF-CCE is conceptually differ- In graph theory, a monoplex network (graph) can be repre- ent. Namely, it works in two steps: first, it denoises sented as an ordered pair, G = (V; E), where V is a set of each network layer by computing its non-negative n = jV j vertices or nodes, and E is a set of m = jEj edges low-dimensional representation. Then it merges the or links between the vertices [25]. An adjacency matrix low-dimensional representations into a consensus representing a graph G is denoted by A 2 f0; 1gn×n, where low-dimensional representation common to all net- Aij = 1 if there is an edge between vertices i and j, and work layers. This makes our method more robust Aij = 0 otherwise. Most of the real-world networks that to noise, and consequently, it yields much stable we consider throughout the paper are represented as edge- clustering results. weighted graphs, G = (V; E; w), where w : E ! R assigns 3) Four efficient algorithms based on four different real values to edges. In this case, the adjacency matrix NMF techniques are developed for NF-CCE using instead of being a binary matrix, is a real one i.e., A 2 Rn×n, the concept of natural gradient [19], [20] and pre- with entries characterizing the strength of association or sented in the form of multiplicative update rules [24] interaction between the network nodes. in Section 3. Although, there is no universally accepted mathematical definition of the community notion in graphs, the probably The advantages of the NF-CCE over the state-of-the-art most commonly accepted definition is the following: a com- in community detection are demonstrated by conducting ex- munity is a set of nodes in a network that are connected tensive experiments on a wide range of real-world multiplex more densely among each other than they are to the rest networks, including biological, social, economic, citation, of the network [14]. Hence, the problem of community phone communication, and brain multiplex networks. In detection is as follows: given an adjacency matrix A of one particular, we compared the clustering performance of our network with n nodes and k communities, find the commu- four methods with 6 state-of-the-art methods and 5 baseline nity assignment of all nodes, denoted by H 2 f0; 1gn×k, methods (i.e., single-layer methods modified for multiplex where Hij = 1 if nodes i belongs to community j, and networks), on 9 different real-world multiplex networks Hij = 0 otherwise. We consider the case of non-overlapping including 3 large-scale multiplex biological networks of 3 communities, where a node can belong to only one commu- different species. Experimental results, in Section 5, indicate k
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