Community Structure In Dynamic Networks Research Articles

Identifying Communities in Dynamic Networks Using Information Dynamics.

Identifying communities in dynamic networks is essential for exploring the latent network structures, understanding network functions, predicting network evolution, and discovering abnormal network events. Many dynamic community detection methods have been proposed from different viewpoints. However, identifying the community structure in dynamic networks is very challenging due to the difficulty of parameter tuning, high time complexity and detection accuracy decreasing as time slices increase. In this paper, we present a dynamic community detection framework based on information dynamics and develop a dynamic community detection algorithm called DCDID (dynamic community detection based on information dynamics), which uses a batch processing technique to incrementally uncover communities in dynamic networks. DCDID employs the information dynamics model to simulate the exchange of information among nodes and aims to improve the efficiency of community detection by filtering out the unchanged subgraph. To illustrate the effectiveness of DCDID, we extensively test it on synthetic and real-world dynamic networks, and the results demonstrate that the DCDID algorithm is superior to the representative methods in relation to the quality of dynamic community detection.

Dynamic Network Community Detection With Coherent Neighborhood Propinquity

The community structure detection in static networks often ignores the dynamic nature of the network and it is difficult to identify the evolution of community structure in dynamic networks. The community structure will converge or split as the nodes and edges change. Understanding the evolution of communities over time is an important issue in the study of social networks. Based on the characteristics of dynamic networks, this paper analyzed the influence of variables in dynamic networks structure. We proposed an Incremental algorithm with Coherent Neighborhood Propinquity in dynamic networks. The algorithm considered the direct and indirect effects of changing nodes in their previous communities. We also considered the coherent neighborhood propinquity and improved the influence range of variable nodes. Comparing with the traditional algorithms, the experimental results showed that the proposed algorithm has better performance and less running time.

Dynamic community detection method based on an improved evolutionary matrix

SummaryMost of networks in real world obviously present dynamic characteristics over time, and the community structure of adjacent snapshots has a certain degree of instability and temporal smoothing. Traditional Temporal Trade‐off algorithms consider that communities found at time t depend both on past evolutions. Because this kind of algorithms are based on the hypothesis of short‐term smoothness, they can barely find abnormal evolution and group emergence in time. In this paper, a Dynamic Community Detection method based on an improved Evolutionary Matrix (DCDEM) is proposed, and the improved evolutionary matrix combines the community structure detected at the previous time with current network structure to track the evolution. Firstly, the evolutionary matrix transforms original unweighted network into weighted network by incorporating community structure detected at the previous time with current network topology. Secondly, the Overlapping Community Detection based on Edge Density Clustering with New edge Similarity (OCDEDC_NS) algorithm is applied to the evolutionary matrix in order to get edge communities. Thirdly, some small communities are merged to optimize the community structure. Finally, the edge communities are restored to the node overlapping communities. Experiments on both synthetic and real‐world networks demonstrate that the proposed algorithm can detect evolutionary community structure in dynamic networks effectively.

Community detection in dynamic signed network: an intimacy evolutionary clustering algorithm

The tremendous development of community detection in dynamic networks have been witnessed in recent years. In this paper, intimacy evolutionary clustering algorithm is proposed to detect community structure in dynamic networks. Firstly, the time weighted similarity matrix is utilized and calculated to grasp time variation during the community evolution. Secondly, the differential equations are adopted to learn the intimacy evolutionary behaviors. During the interactions, intimacy between two nodes would be updated based on the iteration model. Nodes with higher intimacy would gather into the same cluster and nodes with lower intimacy would get away, then the community structure would be formed in dynamic networks. The extensive experiments are conducted on both real-world and synthetic signed networks to show the efficiency of detection performance. Moreover, the presented method achieves better detection performance compared with several better algorithms in terms of detection accuracy.

A multiobjective discrete bat algorithm for community detection in dynamic networks

Some evolutionary based clustering approaches for community detection in dynamic networks need an input parameter to control the preference degree of snapshot and temporal cost. To break the limitation of parameter selection and improve the quality of detecting communities in dynamic network further, a multiobjective discrete bat algorithm (MDBA) is proposed to detect community structure in dynamic networks in this paper. In the proposed algorithm, the bat location updating strategy is designed in discrete form. In addition, turbulence operation and mutation strategy are presented to guarantee the diversity of the population. The non-dominated sorting and crowding distance mechanism are used to keep good solutions during the generation. The experimental results both on synthetic and real networks show that MDBA algorithm is competitive and will get higher accuracy and lower error rate than the compared algorithms.

A multi-similarity spectral clustering method for community detection in dynamic networks.

Community structure is one of the fundamental characteristics of complex networks. Many methods have been proposed for community detection. However, most of these methods are designed for static networks and are not suitable for dynamic networks that evolve over time. Recently, the evolutionary clustering framework was proposed for clustering dynamic data, and it can also be used for community detection in dynamic networks. In this paper, a multi-similarity spectral (MSSC) method is proposed as an improvement to the former evolutionary clustering method. To detect the community structure in dynamic networks, our method considers the different similarity metrics of networks. First, multiple similarity matrices are constructed for each snapshot of dynamic networks. Then, a dynamic co-training algorithm is proposed by bootstrapping the clustering of different similarity measures. Compared with a number of baseline models, the experimental results show that the proposed MSSC method has better performance on some widely used synthetic and real-world datasets with ground-truth community structure that change over time.

Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no 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 limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.

A near-optimal adaptive algorithm for maximizing modularity in dynamic scale-free networks

We introduce A$$^3$$3CS, an adaptive framework with approximation guarantees for quickly identifying community structure in dynamic networks via maximizing Modularity Q. Our framework explores the advantages of the power-law distribution property found in many real-world complex systems. The framework is scalable for very large networks, and more excitingly, possesses approximation factors to ensure the quality of its detected community structure. To the best of our knowledge, this is the first framework that achieves approximation guarantees for the NP-hard Modularity maximization problem, especially on dynamic scale-free networks. To certify our approach, we conduct extensive experiments in comparison with other adaptive methods on both synthesized networks with known community structures and real-world traces including ArXiv e-print citation and Facebook social networks. Excellent empirical results not only confirm our theoretical results but also promise the practical applicability of A$$^3$$3CS in a wide range of dynamic networks.

Stability in flux: community structure in dynamic networks

The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.

Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104. Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.