Microbial communities are enigmatically diverse. We propose a novel view of processes likely affecting microbial assemblages, which could be viewed as the Great American Interchange en miniature : the wholesale exchange among microbial communities resulting from moving pieces of the environment containing entire assemblages. Incidental evidence for such ‘community coalescence' is accumulating, but such processes are rarely studied, likely because of the absence of suitable terminology or a conceptual framework. We provide the nucleus for such a conceptual foundation for the study of community coalescence, examining factors shaping these events, links to bodies of ecological theory, and we suggest modeling approaches for understanding coalescent communities. We argue for the systematic study of community coalescence because of important functional and applied consequences.

Interchange of entire communities: microbial community coalescence.

Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This Letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative clustering coefficient, to quantify the clustering behavior based on the collaborative selection. Accordingly, the clustering properties and clustering-degree correlations are investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.

Empirical analysis of web-based user-object bipartite networks

Restraining the spread of rumors in online social networks (OSNs) has long been an important but difficult problem to be addressed. Currently, there are mainly two types of methods 1) blocking rumors at the most influential users or community bridges, or 2) spreading truths to clarify the rumors. Each method claims the better performance among all the others according to their own considerations and environments. However, there must be one standing out of the rest. In this paper, we focus on this part of work. The difficulty is that there does not exist a universal standard to evaluate them. In order to address this problem, we carry out a series of empirical and theoretical analysis on the basis of the introduced mathematical model. Based on this mathematical platform, each method will be evaluated by using real OSN data. We have done three types of analysis in this work. First, we compare all the measures of locating important users. The results suggest that the degree and betweenness measures outperform all the others in the Facebook network. Second, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Third, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both types of methods together. From both the academic and technical perspective, the work in this paper is an important step towards the most practical and optimal strategies of restraining rumors in OSNs.

To Shut Them Up or to Clarify: Restraining the Spread of Rumors in Online Social Networks

Networked systems with high computational efficiency are desired in many applications ranging from sociology to engineering. Generally, the performance of the network computation can be enhanced by two ways: rewiring and weighting. In this paper, we proposed a new two-modes weighting strategy based on the concept of communication neighbor graph , which takes use of both the local and global topological properties, e.g., degree centrality, betweenness centrality, and closeness centrality. The weighting strategy includes two modes: In the original mode, it enhances the network synchronizability by increasing the weights of bridge edges; whereas in the inverse version, it increases the significance of community structure by decreasing the weights of bridge edges. The scheme of weighting is controlled by only one parameter, i.e., $\alpha$ , which can be easily performed. We test the effectiveness of our model on a number of artificial benchmark networks as well as real-world ones. To the best of our knowledge, the proposed weighting strategy can outperform the existing methods in improving the performance of network computation.

Enhance the Performance of Network Computation by a Tunable Weighting Strategy

LED: A fast overlapping communities detection algorithm based on structural clustering

In this paper, a unique algorithm is proposed to detect overlapping communities in the un-weighted and weighted networks with considerable accuracy. The maximal cliques, overlapping vertex, bridge vertex and isolated vertex are introduced. First, all the maximal cliques are extracted by the algorithm based on the deep and bread searching. Then two maximal cliques can be merged into a larger sub-graph by some given rules. In addition, the proposed algorithm successfully finds overlapping vertices and bridge vertices between communities. Experimental results using some real-world networks data show that the performance of the proposed algorithm is satisfactory.

Uncovering the overlapping community structure of complex networks by maximal cliques

It is common to characterize community structure in complex networks using local neighborhood. Existing related methods fail to estimate the accurate number of nodes present in each community in the network. In this paper a community detection algorithm using local community neighborhood ratio function is proposed. The proposed algorithm predicts vertex association to a specific community using visited node overlapped neighbors. In the beginning, the algorithm detects local communities; then through iterations and local neighborhood ratio function, final communities are detected by merging close related local communities. Analysis of simulation results on real and artificial networks shows the proposed algorithm detects well defined communities in both networks by wide margin.

Community detection using local neighborhood in complex networks

In this paper, we present an alternate algorithm for detecting overlapping community structures in the complex network. Two concepts named the maximal sub-graph and the clustering coefficient between two neighboring communities are introduced. First, all the maximal sub-graphs are extracted from the original networks and then merge them by considering the clustering coefficient of two neighboring maximal sub-graphs. And a new extended modularity is proposed to quantify this algorithm. The other advantage of this algorithm is that the overlapping vertex can be detected. The effectiveness of our algorithm is tested on some real networks. Finally, we compare the computational complexity of this algorithm with selected close related algorithms. The results show that this algorithm gives satisfactory results.

Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient

The participation of a node in more than one community is a common phenomenon in complex networks. However most existing methods, fail to identify nodes with multiple community affiliation, correctly. In this paper, a unique method to define overlapping community in complex networks is proposed, using the overlapping neighborhood ratio to represent relations between nodes. Matrix factorization is then utilized to assign nodes into their corresponding community structures. Moreover, the proposed method demonstrates the use of Perron clusters to estimate the number of overlapping communities in a network. Experimental results in real and artificial networks show, with great accuracy, that the proposed method succeeds to recover most of the overlapping communities existing in the network.

Overlapping community detection using neighborhood ratio matrix

Although many successful algorithms have been designed to discover community structures in network, most of them are dedicated to disjoint and non-overlapping communities. Very few of them are intended to discover overlapping communities, particularly the detection of such communities have hardly been explored in the bipartite networks. In this paper, a novel algorithm is proposed to detect overlapping community structures in bipartite networks. After analyzing the topological properties in bipartite networks, the key bi-communities and free-nodes are introduced. Firstly, some key bi-communities and free-nodes are extracted from the original bipartite networks. Then the free-nodes are allocated into a certain key bi-community by some given rules. In addition, the proposed algorithm successfully finds overlapping vertices between communities. Experimental results using some real-world networks data show that the performance of the proposed algorithm is satisfactory.

Yaozu Cui

Papers

Uncovering the overlapping community structure of complex networks by maximal cliques

Community detection using local neighborhood in complex networks

Detecting overlapping communities in networks using the maximal sub-graph and the clustering coefficient

Overlapping community detection using neighborhood ratio matrix

Uncovering overlapping community structures by the key bi-community and intimate degree in bipartite networks