Detecting community structure based on traffic at node in networks (original) (raw)
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Detecting community structure based on traffic at node in networks.pdf
In the study of networks, such as complex network, social network or biological network; number of different characteristics of many nodes are found common. These characteristics includes small-world property, clustering and community structure, among others. In the context of networks, community structure refers to occurrence of groups of nodes in a network that are more densely connected internally, then with the rest of network. This heterogeneity of connection suggests that network has certain natural division within it. Being able to identify their sub-structure within the network can provide inside into how network function and topology affects each others.
Community Structure based on Node Traffic in Networks
International Journal of Computer Applications, 2013
Finding community structure in networks has been always the prerequisite for the analysis of network structure and its properties. Based on node traffic, an efficient method for calculating betweenness measure is proposed. It is used iteratively to remove edges with high betweenness score from the network, thus splitting network into communities. The score is recalculated after each removal. The algorithm is best suited for networks with traffic generation capabilities.
Community Structure Based on Node Traffic in Networks.pdf
Finding community structure in networks has been always the prerequisite for the analysis of network structure and its properties. Based on node traffic, an efficient method for calculating betweenness measure is proposed. It is used iteratively to remove edges with high betweenness score from the network, thus splitting network into communities. The score is recalculated after each removal. The algorithm is best suited for networks with traffic generation capabilities.
Community structure in social and biological networks
Proceedings of The National Academy of Sciences, 2002
A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of community structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer generated and real-world graphs whose community structure is already known, and find that it detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well-known-a collaboration network and a food web-and find that it detects significant and informative community divisions in both cases.
Characterizing the Community Structure of Complex Networks
PLoS ONE, 2010
Background: Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks.
An indicator for community structure
Arxiv preprint physics/0607159, 2006
An indicator for presence of community structure in networks is suggested. It allows one to check whether such structures can exist, in principle, in any particular network, without a need to apply computationally cost algorithms. In this way we exclude a large class of networks that do not possess any community structure.
Community structures evaluation in complex networks: A descriptive approach
Evaluating a network partition just only via conventional quality metrics – such as modularity, conductance or normalized mutual of information – is usually insufficient. Indeed, global quality scores of a network partition or its clusters do not provide many ideas about their structural characteristics. Furthermore, quality metrics often fail to reach an agreement especially in networks whose modular structures are not very obvious. Evaluating the goodness of network partitions in function of desired structural properties is still a challenge. Here, we propose a methodology that allows one to expose structural information of clusters in a network partition in a comprehensive way, thus eventually helps one to compare communities identified by different community detection methods. This descriptive approach also helps to clarify the composition of communities in real-world networks. The methodology hence bring us a step closer to the understanding of modular structures in complex networks.
A Flow Propagation Method For Detection of Local Community
This paper is using an algorithm (Flow-Pro) for finding the node community in a complex network without need to know the information of the whole graph. In general, the researchers supposed their network based on undirected graph and the edge weight for each two connected, neighbour nodes are equal to 1, otherwise it will be 0. In the first step, the function implemented to give community, according to the stored flow. Synthetic data were used with 20,000 nodes. Also, 20 communities had been used. In this paper, edges weights N x N for network used, where N denotes the number of nodes. The total number of messages that produced from the flow algorithm for 1000 nodes was calculated , where for 20000 nodes in our result was (45,582,924) messages. 258 عشر الخامس العلمي المؤتمر وقائع / الجامعة المنصور كلية المتخصص 23 -24 نيسان 2012 عشر الخامس العلمي المؤتمر وقائع / الجامعة المنصور كلية المتخصص 23 -24 نيسان 2012 259
Encyclopedia of Complexity and Systems Science, 2009
Graph vertices are often organized into groups that seem to live fairly independently of the rest of the graph, with which they share but a few edges, whereas the relationships between group members are stronger, as shown by the large number of mutual connections. Such groups of vertices, or communities, can be considered as independent compartments of a graph. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. The task is very hard, though, both conceptually, due to the ambiguity in the definition of community and in the discrimination of different partitions and practically, because algorithms must find "good" partitions among an exponentially large number of them. Other complications are represented by the possible occurrence of hierarchies, i.e. communities which are nested inside larger communities, and by the existence of overlaps between communities, due to the presence of nodes belonging to more groups. All these aspects are dealt with in some detail and many methods are described, from traditional approaches used in computer science and sociology to recent techniques developed mostly within statistical physics.
Detecting_communities_in_large_networks.pdf
We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns. r Measurements and exact results concerning the clustering patterns of networks mainly concern the occurrence of regular motifs [1-4] and their correlations . However, many social and information networks, such as the World Wide Web, turn out to be approximately partitioned into communities of irregular shape: for example web pages focusing on similar topics are strongly mutually connected and have a weaker linkage to the rest of the Web. The design of methods to partition a ARTICLE IN PRESS www.elsevier.com/locate/physa 0378-4371/$ -see front matter r