A network community refers to a group of network nodes within which the links connecting them are dense but between which they are sparse. A network community mining problem (NCMP for short) can be stated as the problem of finding all such communities from a given network. A large variety of problems can be translated into NCMPs, ranging from graph partition applications, such as VLSI layout [1, 2], large-scale scientific computing [3, 4], load balance for distributed computing [5, 6], and image segmentation [19], to complex network analysis, such as social network analysis [7, 8, 9], biological network analysis[11, 12, 24], and Web pages clustering [13, 14, 15]. Network communities in different application contexts may imply different meanings and serve different purposes. For example, they may be sets of electronic units closely placed on a VLSI circuit board, collections of processes that frequently communicate with each other, segments of an image, circles of a society within which people share common interests and keep frequent contacts, classes of Web pages related to common topics, or groups of proteins exhibiting similar functions. Discovering such hidden patterns from networks will enable us to better understand the structural and/or functional characteristics of networks and to more effectively utilize them. The remainder of this chapter is organized as follows: Section 2 introduces some of the existing methods for solving NCMPs. Section 3 presents several interesting applications of NCMPs as related to social network analysis. Finally, Section 4 concludes the chapter.

http://nlpr-web.ia.ac.cn/2010papers/%E5%BC%80%E6%94%BE%E8%AF%BE%E9%A2%98/%E5%9B%BD%E5%86%85%E4%BC%9A%E8%AE%AE/Bo%20Yang,%20Discovering%20Communities%20from%20Social%20Networks%20Methodologies%20and%20Applications.pdf

Discovering Communities from Social Networks: Methodologies and Applications

Many complex networks, including human societies, the Internet, the World Wide Web and power grids, have surprising properties that allow vertices (individuals, nodes, Web pages, etc.) to be in close contact and information to be transferred quickly between them. Nothing is known of the emerging properties of animal societies, but it would be expected that similar trends would emerge from the topology of animal social networks. Despite its small size (64 individuals), the Doubtful Sound community of bottlenose dolphins has the same characteristics. The connectivity of individuals follows a complex distribution that has a scale-free power-law distribution for large k. In addition, the ability for two individuals to be in contact is unaffected by the random removal of individuals. The removal of individuals with many links to others does affect the length of the information path between two individuals, but, unlike other scale-free networks, it does not fragment the cohesion of the social network. These self-organizing phenomena allow the network to remain united, even in the case of catastrophic death events.

The emergent properties of a dolphin social network

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods.

/pdf/a-node-influence-based-label-propagation-algorithm-for-2wm5uvxqeb.pdf

A node influence based label propagation algorithm for community detection in networks.

Occupational health and safety issues in operations management: A systematic and citation network analysis review

Community detection is a very important problem in social network analysis. Classical clustering approach, K -means, has been shown to be very efficient to detect communities in networks. However, K -means is quite sensitive to the initial centroids or seeds, especially when it is used to detect communities. To solve this problem, in this study, we propose an efficient algorithm K -rank, which selects the top- K nodes with the highest rank centrality as the initial seeds, and updates these seeds by using an iterative technique like K -means. Then we extend K -rank to partition directed, weighted networks, and to detect overlapping communities. The empirical study on synthetic and real networks show that K -rank is robust and better than the state-of-the-art algorithms including K -means, BGLL, LPA, infomap and OSLOM.

An efficient community detection method based on rank centrality

Complex Network Clustering Algorithms: Complex Network Clustering Algorithms

Community structure is one of the most important topological properties in complex networks. The network clustering problem (NCP) refers to the detection of network community structures, and many practical problems can be modeled as NCPs. So far, lots of network clustering algorithms have been proposed. However, further improvements in the clustering accuracy, especially when discovering reasonable community structure without prior knowledge, still constitute an open problem. Building on Markov random walks, the paper addresses this problem with a novel ant colony optimization strategy, named as RWACO, which improves prior results on the NCPs and does not require knowledge of the number of communities present on a given network. The framework of ant colony optimization is taken as the basic framework in the RWACO algorithm. In each iteration, a Markov random walk model is taken as heuristic rule. All of the ants’ local solutions are aggregated to a global one through clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually, this converges to a solution where the underlying community structure of the complex network will become clearly visible. The performance of algorithm RWACO was tested against a set of benchmark computer-generated networks, and as well on real-world network data sets. Experimental results confirm the validity and improvements of this approach.

Ant Colony Optimization Based on Random Walk for Community Detection in Complex Networks: Ant Colony Optimization Based on Random Walk for Community Detection in Complex Networks

Modeling and Inferring Epidemic Networks Based on Autonomy Oriented Computing: Modeling and Inferring Epidemic Networks Based on Autonomy Oriented Computing

Bo Yang

Papers

Complex Network Clustering Algorithms: Complex Network Clustering Algorithms

Ant Colony Optimization Based on Random Walk for Community Detection in Complex Networks: Ant Colony Optimization Based on Random Walk for Community Detection in Complex Networks

Modeling and Inferring Epidemic Networks Based on Autonomy Oriented Computing: Modeling and Inferring Epidemic Networks Based on Autonomy Oriented Computing