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Social networks are no longer a place where you can spend leisure time and chat with friends. It is also a business instrument in work with their audiences to increase brand recognition, total result from marketing and move sales up. For this purposes it's needed to make thorough analysis of the target audience, scan dozens of user profiles, reveal their interests, positions and estimate users LTV...
Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such...
In real life networks like social and biological networks, the network is said to have community structure if the vertices of the network can be partitioned into groups of nodes such that each group contains the nodes that are densely connected in the original graph that represents this network. These groups are referred to as communities. Detecting and identifying communities in networks is essential...
Nowadays, social network sites, such as Facebook and Twitter, have tremendous number of users in their repositories. Having this huge amount of data requires analyzing them to get statistics about the users and their interests. In this paper, we propose a new algorithm that clusters the nodes in social networks into communities based on their geodesic location and the similarity between their interests...
One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection algorithms. However, it usually leads to high computation complexity. In this paper, a novel algorithm based on the MapReduce model and the label propagation progress with the strategy of incremental related vertices is proposed, which is called PLPIRV (Parallel...
In this paper, we study the multi-leader selection problem in complex networks. While selecting a single leader can be done via various centrality measures, selecting multiple leaders is much more involved than a simple order of the nodes in terms of centrality measures. In many situations, it is often desirable to see that the multiple leaders selected are as representative as possible. Motivated...
Social networks have gained a lot of interest in recent literature due to the huge amount of data that can be extracted from them. With this ever growing data, emerged the need for techniques to handle it and analyze it. Several papers have proposed many techniques to analyze a given social network from several aspects. Communities are a crucial property in social networks and community detection...
Community detection is one of the most important ways that reflect the structure and mechanism beneath the social network. The overlapping communities are more in line with the reality of social network. In the society, the phenomenon of some members shared membership of different communities reflects as overlapping communities in the network. Facing big data network, it is a challenging and computationally...
A number of approaches based on symmetric nonnegative matrix factorization (SNMF) have been proposed to improve the performance and the interpretability of community detection. Due to the nature of NMF, the partition results obtained by conventional NMF without post processing are soft assignments of nodes w.r.t. communities, which demonstrates overlapping of communities. Based on the traditional...
With the rapid development of the Internet, information can be widely diffused through the social network. It is critical to locate the information source under some circumstances in nowadays growing network. In this paper, we propose an effective algorithm to locate the information source. First, we apply Fiedler vector to partition the network into several node clusters, where observers are selected...
The flood of real time social data, generated by various social media applications and sensors, is enabling researchers to gain critical insights into important social modeling and analysis problems such as the evolution of social relationships and analysis of emergent social processes. However, current computational tools have to address the grand challenge of analyzing large and dynamic social networks...
Finding communities or clusters in social networks is a famous topic in social network analysis. Most algorithms are limited to static snapshots so they cannot handle dynamics within the underlying graph. In this paper, we present a modification of the Lou-vain community detection method to handle changes in the graph without rerunning the full algorithm. Also, we adapted the Louvain greedy approach...
Community and cluster detection is a popular field of social network analysis. Most algorithms focus on static graphs or series of snapshots. In this paper we present an hierarchical algorithm, which detects communities in dynamic graphs. The method is based on the shortest paths to high-connected nodes, so called hubs. Due to local message passing, we can update the clustering results with low computational...
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...
Due to the growing presence of large-scale and streaming graphs such as social networks, graph sampling and clustering play an important role in many real-world applications. One key aspect of graph clustering is the evaluation of cluster quality. However, little attention has been paid to evaluation measures for clustering quality on samples of graphs. As first steps towards appropriate evaluation...
Currently, network perspective is rapidly becoming trends for representing and analyzing problems across all of the domains from natural science to engineering and management, with no exception in supply chain management. Treating supply chain system as a network give a good advantages since there are a lot of method in network theory that can be applied to give a quantitative measurement. In turns,...
In order to discover overlapping community structure of social networks more effectively, this paper proposes an algorithm of overlapping community detection based on peak density. The algorithm firstly calculates the matrixes of network topology information distance, and then calculates the local density for each node within a given radius. And then cluster centers are those points which have high...
Community detection has attracted considerable attention crossing many areas as it can be used for discovering the structure and features of complex networks. With the increasing size of social networks in real world, community detection approaches should be fast and accurate. The Label Propagation Algorithm (LPA) is known to be one of the near-linear solutions and benefits of easy implementation,...
The study of social networks has gained much interest from the research community in recent years. Most of the existing algorithms proposed for communities determination are based on the topological features of social networks. In this paper, we propose a new objective function where we incorporate the value of structure, semantic similarity, a bees colonies algorithm to optimize our objective function...
In recent years community detection has been a hot research topic in network science, which helps to explain the characteristics of the network structure. This paper analyzed the effect of vertices with high influence in community detection, and found that such vertices have different roles in different networks. A variable influence community detection algorithm based on PageRank is proposed in this...
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