Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.
Optimization can be used to identify minimum cost community assignments in dynamic graphs. FacetNet is a framework for analyzing communities in dynamic networks based on an optimization of snapshot costs. It is guaranteed to converge to a local optimal solution; however, its convergence speed is slow, and it needs to be initialized with the number of communities which is usually unknown in practice. @cite_2 modeled dynamic community detection as a multi-objective optimization problem. Their approach is parameter free and uses evolutionary clustering to optimize a dual objective function. The first objective selects for highly modular structures at the current time step, and the second minimizes the differences between community structures in the current and previous time steps. D-GT also uses a stochastic optimization procedure, but all of the agents individually optimize their utilities based on local network information.
@cite_2
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Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game-theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game-Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity. Community detection is the task of detecting hidden communities from observed interactions. Guaranteed community detection has so far been mostly limited to models with non-overlapping communities such as the stochastic block model. In this paper, we remove this restriction, and provide guaranteed community detection for a family of probabilistic network models with overlapping communities, termed as the mixed membership Dirichlet model, first introduced by (2008). This model allows for nodes to have fractional memberships in multiple communities and assumes that the community memberships are drawn from a Dirichlet distribution. Moreover, it contains the stochastic block model as a special case. We propose a unified approach to learning these models via a tensor spectral decomposition method. Our estimator is based on low-order moment tensor of the observed network, consisting of 3-star counts. Our learning method is fast and is based on simple linear algebraic operations, e.g., singular value decomposition and tensor power iterations. We provide guaranteed recovery of community memberships and model parameters and present a careful finite sample analysis of our learning method. As an important special case, our results match the best known scaling requirements for the (homogeneous) stochastic block model. We propose two approximation algorithms for identifying communities in dynamic social networks. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Recently, we have proposed an optimization-based framework for modeling dynamic community structure. Also, we have proposed an algorithm for finding such structure based on maximum weight bipartite matching. In this paper, we analyze its performance guarantee for a special case where all actors can be observed at all times. In such instances, we show that the algorithm is a small constant factor approximation of the optimum. We use a similar idea to design an approximation algorithm for the general case where some individuals are possibly unobserved at times, and to show that the approximation factor increases twofold but remains a constant regardless of the input size. This is the first algorithm for inferring communities in dynamic networks with a provable approximation guarantee. We demonstrate the general algorithm on real data sets. The results confirm the efficiency and effectiveness of the algorithm in identifying dynamic communities. Identification of module or community structures is important for characterizing and understanding complex systems. While designed with different objectives, i.e., stochastic models for regeneration and modularity maximization models for discrimination, both these two types of model look for low-rank embedding to best represent and reconstruct network topology. However, the mapping through such embedding is linear, whereas real networks have various nonlinear features, making these models less effective in practice. Inspired by the strong representation power of deep neural networks, we propose a novel nonlinear reconstruction method by adopting deep neural networks for representation. We then extend the method to a semi-supervised community detection algorithm by incorporating pairwise constraints among graph nodes. Extensive experimental results on synthetic and real networks show that the new methods are effective, outperforming most state-of-the-art methods for community detection.