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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...
In typical Opportunistic Networking (OppNets) scenarios, mobile devices collaborate to cooperatively disseminate data toward interested nodes. However, the limited resources and knowledge available at each node, compared to possibly vast amounts of data to be delivered, makes it difficult to devise efficient dissemination schemes. Recent solutions propose to use data dissemination algorithms built...
This paper discusses the problem of clustering data changing over time, a research domain that is attracting increasing attention due to the increased availability of streaming data in the Web 2.0 era. In the analysis conducted throughout the paper we make use of the kernel spectral clustering with memory (MKSC) algorithm, which is developed in a constrained optimization setting. Since the objective...
In this study we analyse complete networks derived from field survey and market research through proposing an efficient methodology based on proximity graphs and clustering techniques enhanced with a new community detection algorithm. The specific context is the charity and Not-For-Profit sector in Australia and consumer behaviours within this context. To investigate the performance of this methodology...
Social network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a graph is a popular technique that has been used for centrality analysis, for communities discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run i.e., large...
Community-detection is a powerful approach to uncover important structures in large networks. Since networks often describe flow of some entity, flow-based community-detection methods are particularly interesting. One such algorithm is called Info map, which optimizes the objective function known as the map equation. While Info map is known to be an effective algorithm, its serial implementation cannot...
Previous works have analyzed the cluster organization of the cat cortical network using both traditional multidimensional scaling methods and evolutionary optimization algorithms. Interestingly, the evolutionary optimization principle of previous works is based on the modularity measure used to find communities in network with global algorithms. In this paper, we deepen this point taking into account...
A Social network structure contains several nodes which are connected based on the relationships. Network community mining methods are used discover all hidden communities in distributed social networks based on some criteria. Several algorithms have been developed to solve the hidden community mining problem. In a given network the links between the nodes are opaque, but few nodes are thin. Finding...
As community detection having been a hot issue in recent years, firstly the correlate clustering technologies based on local information are studied and summarized. Secondly, the label propagation algorithm that is short for LPA is researched and analyzed in depth. Finally, for the random strategy of LPA results that the network partition is always not optimal, we introduces the concept of the similarity...
Detection of community structure in a social network is important for understanding the structure and dynamics of the network. Yet, most community detection algorithms do not take attributes of nodes and connections into consideration, or only make use of connections' attributes information, not fully capturing the richness of the information contained in the data. Thus, by exploring the information...
The identification of communities in complex networks is important to many fields including medicine, social science, national security, and marketing. A community structure facilitates the identification of hidden relations in networks that go beyond simple topological features. Current detection algorithms are centralized and scale very poorly with the number of nodes and edges present in the network...
In data mining, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: agglomerative and divisive. In this paper we shall introduce a new optimal selection method based on the well-known Max-Flow Min-Cut theorem, which also works for the hierarchically structure with overlapping. A...
Managing large-scale networks involving users and applications is challenging due to the complexity and dynamic nature of the heterogeneous graphs. How to quickly identify the meaningful changes and hidden anomalous activities in the spatiotemporally dynamic network graphs is essential in many aspects of network management, such as security, performance and troubleshooting. In this paper, we explore...
Community mining has been the focus of many recent researches on dynamic social networks. In this paper, we propose a clustering based improved ant colony algorithm (CIACA) for community mining in social networks. The CIACA combines the local pheromone update rule with the global update rule and utilizes heuristic function to adjust the clustering solution dynamically, assisted by decay coefficient...
The modularity function is a widely used measure for the quality of a graph clustering. Finding a clustering with maximal modularity is NP-hard. Thus, only heuristic algorithms are capable of processing large datasets. Extensive literature on such heuristics has been published in the recent years. We present a fast randomized greedy algorithm which uses solely local information on gradients of the...
The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible...
The investigation of community structures in networks is an important issue in many domains and disciplines. However, Most of the present algorithms consider only structure of the network, ignoring some additional conditions such as direction, weight, semantic, etc. In this paper the behaviors of each vertex are focus. Based on the previous work, two limitations of swarm similarity in closely community...
Cluster analysis describes the division of a dataset into subsets of related objects, which are usually disjoint. There is considerable variety among the different types of clustering algorithms. Some of these clustering algorithms represent the dataset as a graph, and use graph-based properties to generate the clusters. However, many graph properties have not been explored as the basis for a clustering...
A community in a network is a subset of vertices densely connected to each other, but less connected to the vertices outside. Many different approaches have been developed to find such structures in a given network, but the main drawback of most of the available algorithms is that they are computationally demanding and their complexity is usually an exponentially increasing function of the number...
The investigation of community structures in networks is an important issue in many domains and disciplines.Closely communicating community is different from the traditional community which emphasize particularly on structure or context. The definition of closely communicating community and measuring method are introduced firstly. Based on the previous work, the closely communicating community detection...
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