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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...
The study of embedded structure of communities in social and information networks is an extensive studies in this domain and vast variety of community detection methods have been proposed. In this paper we proposed a distributed approach for local and overlapping community detection based on the game theory. In our method, each node is a player and there is an iterative cycle in which players can...
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...
The W3C Data on the Web Best Practices Working Group is standardizing the Data Quality Vocabulary (DQV) for expressing data quality of Web-published datasets. As proposed in the DQV specification, quality annotations on datasets, one kind of quality information described using DQV, are achieved through Web annotations. Meanwhile, the W3C Web Annotation Working Group is creating a standard Web Annotation...
Efficient organization and analysis of academic information has many advantages. Most scholar retrieval systems appeared these years can perform keyword-based paper search. However, performing large-scale expert and paper retrieval is an intractable problem. Here we present a platform that can not only reduce the workload of researchers when searching academic literature, but also promote academic...
Skyline queries are currently the most notable type of multi-criteria search algorithm. A skyline query returns all of the data points in a given a dataset that are not dominated by other data points. However, this type of query is limited by the fact that the number of results cannot be controlled. In some cases, this can result in an excessive number of results, whereas other cases result in an...
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...
Influence maximization specifies a set of nodes that maximizes the influences in social networks. The influence maximization problem due to its importance in targeted marketing has been explored by many researchers. All proposed algorithms are not scalable and are too time consuming for large-scale social network. In this paper, an efficient and fast algorithm called ComPath+ is proposed for influence...
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...
Blockmodelling is a technique whose aim is to identify meaningful structure in networks. Community finding is a type of blockmodelling in so far as it focuses on identifying dense subgraph structure. Generalised blockmodelling allows an analyst to explicitly control the type of extracted structure. When compared to the well studied community-finding problem, generalised blockmodelling algorithms lag...
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,...
Complex social network analysis methods have been applied extensively in various domains including online social media, biological complex networks, etc. Complex social networks are facing the challenge of information overload. The demands for efficient complex network analysis methods have been rising in recent years, particularly the extensive use of online social applications, such as Flickr, Facebook...
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...
In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs...
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