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Social networks are usually analyzed and mined without taking into account the presence of missing values. In this article, we consider dynamic networks represented by sequences of graphs that change over time and we study the robustness and the accuracy of the community detection algorithms in presence of missing edges. We assume that the network evolution can provide a complementary information...
Community detection is a hot topic for researchers in the fields including graph theory, social networks and biological networks. Generally speaking, a community refers to a group of densely linked nodes in the network. Nodes usually have more than one community label, indicating their multiple roles or functions in the network. Unfortunately, existing solutions aiming at overlapping-community-detection...
The analysis of communities and their evolution in dynamic networks is a challenging research with broad applications. The recent studies have found that the overlaps between communities are more densely connected than the non-overlapping parts in some real networks. The findings are different from the present concepts of the overlapping communities. Existing methods may fail to detect this kind of...
At present in most field's data sets, spectral clustering community detection algorithm is difficult to predict the number of clustering problems, this paper proposes a community detection algorithm based on multi-domain adaptive spectral clustering (MDASC). Firstly based on the local node density composition, combined with graph edge-betweenness structural similarity matrix, normalized spectral clustering,...
The discovery of communities in complex networks is a challenging problem with various applications in the real world. Classic examples of networks include transport networks, the immune system, human brain and social networks. Given a certain grouping of nodes into communities, a good measure is needed to evaluate the quality of the community structure based on the definition that a strong community...
Although many different community detection algorithms have been proposed to detect community structures in complex networks, how to effectively detect community structures is still a great challenge. Seed-centric methods is one of the most effective solutions for community detection. To more, in this paper, we propose a novel density-based seed expansion algorithm, namely, DenSeC, which can easily...
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...
We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides...
Heterogeneous networks have become an important model to represent complex network. However, many existing community detection methods for dynamic network are hardly applied in heterogeneous networks. In this paper, we present a multi-view learning based algorithm for dynamic heterogeneous networks, which treats network individually, combines heterogeneous information and improves the quality. Compared...
How the hotspot or congestion area evolves in a large scale complex networks is still not clear. The prediction of such behavior is more difficult. In this paper, the classical Fast-Newman algorithms for community detection is improved by considering node weight and edge weight in the network model. The evolution of the communities are reconstructed from the network trace. The relation between the...
During the evolution of social network, there is a social network phenomenon that small communities also become important. Generally, each community has its own characteristics of internal correlation and relation. Accurate division of whole social networks into multiple small communities may help improve the quality of social network services as whole. With the comparison among substantial community...
Methods for clustering static graphs cannot always be transferred straight forward to dynamic scenarios. A typical approach is to reduce the number of updates by reusing results of previous iterations. But are there natural ways to implement dynamic graph clustering? This paper proposes a method, which was derived by graph based ant colony algorithms. Similar to other clustering algorithms, multiple...
The ongoing issue in social network is detecting the communities for large data sets efficiently, stabilizing the communities in the network so that we get same structure over different runs for same network data set pose a challenging problem in the research community. Although, there were various attempts in the past to come out with an efficient and cost effective algorithm that can perform an...
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...
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...
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...
The patterns of movement used by Mobile Ad-Hoc networks are application specific, in the sense that networks use nodes which travel in different paths. When these nodes are used in experiments involving social patterns, such as wildlife tracking, algorithms which detect and use these patterns can be used to improve routing efficiency. The intent of this paper is to introduce a routing algorithm which...
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 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...
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