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Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks PHYSICAL REVIEW X 6, 031005 (2016) Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks Amir Ghasemian,1 Pan Zhang,2 Aaron Clauset,1,3,4 Cristopher Moore,4 and Leto Peel5,6 1Department of Computer Science, University of Colorado, Boulder, Colorado 80309, USA 2CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China 3BioFrontiers Institute, University of Colorado, Boulder, Colorado 80305, USA 4Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA 5ICTEAM, Université catholique de Louvain, Avenue George Lemaître 4, B-1348 Louvain-la-Neuve, Belgium 6naXys, University of Namur, Rempart de la Vierge 8, 5000 Namur, Belgium (Received 2 November 2015; revised manuscript received 17 May 2016; published 13 July 2016) The detection of communities within a dynamic network is a common means for obtaining a coarse- grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information. DOI: 10.1103/PhysRevX.6.031005 Subject Areas: Complex Systems, Interdisciplinary Physics, Statistical Physics I. INTRODUCTION coarse graining of the network, revealing the large-scale structure of the system. In the simple case of static networks, Many complex systems can be represented as networks, we now have rigorous methods for accomplishing this that is, as a set of elements characterized by pairwise task, using Bayesian techniques and probabilistic generative interactions. Examples of networks are plentiful and models [9–15]. Importantly, we also have a precise math- include friendships or communication in a social network, ematical understanding of when they can or cannot succeed regulatory interactions among genes, transportation [14,16]. However, real-world networks are rarely simple, between cities, and hyperlinks between documents or and are often accompanied by auxiliary data, such as weights websites. Furthermore, many, perhaps even most, of these on edges [17] or metadata on nodes [18–22]. Extending the networks are dynamic in nature, and their evolving struc- – rigorous results for static networks to these richer graph ture is often represented as a sequence of graphs [1 8]. structures remains an important direction of study. Here, we A common step in analyzing the structure of such net- focus on the question of dynamic networks, in which each works is the detection of communities, in which we seek to node’s connections may change over time. divide a network into groups of nodes that play similar Community detection in dynamic networks inherits structural roles. A good division should provide a structural many of the challenges of static networks, including learning the number of communities, their sizes and node membership, and the pattern of connections among com- munities, e.g., assortative or disassortative (or, in physical Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distri- terms, ferromagnetic or antiferromagnetic). However, it bution of this work must maintain attribution to the author(s) and also poses new challenges, as both the network topology the published article’s title, journal citation, and DOI. and the community memberships may evolve over time. 2160-3308=16=6(3)=031005(9) 031005-1 Published by the American Physical Society GHASEMIAN, ZHANG, CLAUSET, MOORE, and PEEL PHYS. REV. X 6, 031005 (2016) Community detection in dynamic networks has a long network snapshot aggregates these calls over a month. In history, and a number of techniques have been previously this case, belief propagation (BP) algorithms, like the ones developed. For instance, there are variants of multilayer or we develop here, are often asymptotically optimal, and we temporal modularity maximization [5,23,24], non-negative may use the cavity method to compute the detectability matrix or tensor factorization [3,6,8,25,26], minimum threshold exactly. While our results are not mathematically description length [27,28], and probabilistic models rigorous, we believe that they can be made so using the [4,7,29–33]. References [34,35] provide more comprehen- techniques of Refs. [16,48–50]. sive reviews of this work. Approaches for detecting Finally, we give two principled and efficient algorithms for communities in multiplex networks are also relevant detecting communities in real dynamic networks. The first [36–45], as dynamic networks are a special case of algorithm uses BP to pass messages between neighbors both multiplex networks, in which the layers are organized in within a network at a particular time and between consecutive a linear sequence. However, despite these varied efforts, up networks in order to integrate information over the network’s to now we have lacked a theoretical understanding of the time series in an optimal way. We then linearize BP to obtain optimality of these techniques, when or how they tend to a second spectral algorithm, based on a dynamical version fail, or whether there are fundamental limits to detecting of the nonbacktracking matrix [50,51]. Through numerical community structure in dynamic networks. experiments, we confirm our theoretical calculations by Here, we answer these questions by deriving a precise showing that these algorithms accurately recover the true threshold on the detectability of communities in dynamic community structure in dynamic networks all the way down networks, whose location depends only on the rate of to the generalized detectability threshold. change of the community structure and on its strength. Below this sharp threshold, we claim no efficient algorithm II. DYNAMIC STOCHASTIC BLOCK MODEL can recover the true communities better than chance. Furthermore, we give two algorithms that are optimal in The stochastic block model is a classic model of the sense that they succeed all the way down to this community structure in static networks [46,47]. To obtain threshold. These results generalize the theoretical insights a theoretical understanding of detectability in dynamic of Refs. [14,15] for community detection in static networks networks, we use a variant of the stochastic block model in to the dynamic setting, in which detectability depends on which the community labels of nodes change over time, but where edges are independent conditioned on these labels. both spatial and temporal coupling between nodes. The This particular model is also a special case of several mathematical tools we use to obtain these results are also models previously introduced for community detection in general, and could be used to obtain similar extensions to dynamic networks [4,7,29–31]. A crucial feature of the networks with other types of auxiliary information. variant we study is that it captures the dynamic behavior of Our approach exploits the powerful tools of probabilistic changing community labels but is analytically tractable. generative models and Bayesian inference, which we use to Our model generates a dynamic sequence of graphs study the limits of the community detection problem using G t (V;E t ), with 1 ≤ t ≤ T. There are V n the cavity method of statistical physics. We begin with the ð Þ¼ ð Þ j j¼ nodes divided into k groups. Each graph has its own well-known stochastic block model [46,47], a generative group assignment, represented by an n-dimensional vector model for static networks with community structure. We of labels fg ðtÞ ∈ f1; …;kgji ∈ Vg. To generate this note that there are several dynamic variants of this model i sequence, we start by drawing g ð1Þ from a prior distribu- [29–31] and its mixed-membership version [7], and the i tion, where each node has initial probability q of being in variant that we analyze here is a special case of some of r community 1 ≤ r ≤ k. In successive steps t>1, each node these models. Specifically, we mathematically study a updates its label according to a transition matrix τ, moving model in which nodes change their community member- to group r from group s with probability τ . Finally, the ship over time according to a Markov process, and edges rs edges EðtÞ are generated independently for each t accord- are generated independently at each time step. As a result, ing to a k × k matrix p, connecting each pair of nodes i, j the network of connections between nodes at different at time t with probability p . The likelihood of the times is locally treelike. giðtÞ;gjðtÞ In many real-world systems, edge occurrence can corre- graph sequence is then late across time [1]. In this case, however, our model can τ still be applied if the edges have a time scale that is short PðfGðtÞg; fgðtÞgjp; q; Þ relative to the time windows over which the interactions YT are aggregated, which returns us to a setting in which the ¼ P(gð1Þ) P(gðtÞjgðt − 1Þ) t¼2 dynamic network will be locally treelike.
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