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Recently, due to the popularity of Web 2.0, considerable attention has been paid to the opinion leader discovery in social network. By identifying the opinion leaders, companies or governments can manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, mining opinion...
In this paper, an Aggregation-Division Model (ADM) is proposed to simulate the clustering characteristics of nodes in cyberspace and to describe the dynamic evolution process of the community system structure. Based on Cluster-Cluster Aggregation (CCA) model, using the random walk and collision of discrete single particle to simulate the communication and relationship establishment of nodes in social...
In recent years, community detection in overlapping weighted network became a research challenge. In real networks, a node can belong to two or more communities. Therefore, in this paper, we aim to address the above-mentioned problem by proposing a method to improve the modularity in overlapping weighted networks. The proposed method is based on optimizing a fitness function and fuzzy belonging degree...
Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology...
The problem of finding connected components in a graph is common to several applications dealing with graph analytics, such as social network analysis, web graph mining and image processing. The exponentially growing size of graphs requires the definition of appropriated computational models and algorithms for their processing on high throughput distributed architectures. In this paper we present...
Identifying communities has always been a fundamental task in analysis of complex networks. Many methods have been devised over the last decade for detection of communities. Amongst them, the label propagation algorithm brings great scalability together with high accuracy. However, it has one major flaw; when the community structure in the network is not clear enough, it will assign every node the...
We study the important problem of source localization in the context of information spreads in large social networks. Specifically, we design a Maximum-Likelihood source localization algorithm that is especially suited to large social networks. Our proposed algorithm requires about 3% fewer sensor nodes than other single stage algorithms for the same level of accuracy in detection. For practical social...
In this paper, we propose an algorithm that detects overlapping communities in networks (graphs) based on a simple node behavior model. The key idea in the proposed algorithm is to find communities in an agglomerative manner such that every detected community S has the following property: For each node i ∈ S, we have (i) the fraction of nodes in S \ {i} that are neighbors of node i is greater than...
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