Researchers have devoted themselves to exploring static features of social networks and further discovered many representative characteristics, such as power law in the degree distribution and assortative value used to differentiate social networks from nonsocial ones. However, people are not satisfied with these achievements and more and more attention has been paid on how to uncover those dynamic characteristics of social networks, especially how to track community evolution effectively. With these interests, in the paper we firstly display some basic but dynamic features of social networks. Then on its basis, we propose a novel core-based algorithm of tracking community evolution, CommTracker, which depends on core nodes to establish the evolving relationships among communities at different snapshots. With the algorithm, we discover two unique phenomena in social networks and further propose two representative coefficients: GROWTH and METABOLISM by which we are also able to distinguish social networks from nonsocial ones from the dynamic aspect. At last, we have developed a social network model which has the capabilities of exhibiting two necessary features above.
As to dynamic graph mining, Tanya Y.Berger-Wolf and Jared Saia study community evolution based on node overlapping @cite_19 ; John Hopcroft and Omar Khan propose a method which utilizes nature community" to track evolution @cite_7 . However, both methods have to set some parameters, which is too difficult to be adaptive to various situations. In contrast, suggests the notion of parameter free data mining @cite_18 . Jimeng Sun's GraphScope is a parameter-free mining method of large time-evolving graphs @cite_13 , using information theoretic principles. Our method in the paper shares the same spirit.
@cite_19 @cite_18 @cite_13 @cite_7
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Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process.Data mining algorithms should have as few parameters as possible, ideally none. A parameter-free algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics and computational theory hold great promise for a parameter-free data-mining paradigm. The results are motivated by observations in Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen or so lines of code. We will show that this approach is competitive or superior to the state-of-the-art approaches in anomaly interestingness detection, classification, and clustering with empirical tests on time series DNA text video datasets. How can we find communities in dynamic networks of socialinteractions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity time-points in such streams of graphs, in an on-line, any-time fashion? We propose GraphScope, that addresses both problems, using information theoretic principles. Contrary to the majority of earlier methods, it needs no user-defined parameters. Moreover, it is designed to operate on large graphs, in a streaming fashion. We demonstrate the efficiency and effectiveness of our GraphScope on real datasets from several diverse domains. In all cases it produces meaningful time-evolving patterns that agree with human intuition. We are interested in tracking changes in large-scale data by periodically creating an agglomerative clustering and examining the evolution of clusters (communities) over time. We examine a large real-world data set: the NEC CiteSeer database, a linked network of >250,000 papers. Tracking changes over time requires a clustering algorithm that produces clusters stable under small perturbations of the input data. However, small perturbations of the CiteSeer data lead to significant changes to most of the clusters. One reason for this is that the order in which papers within communities are combined is somewhat arbitrary. However, certain subsets of papers, called natural communities, correspond to real structure in the CiteSeer database and thus appear in any clustering. By identifying the subset of clusters that remain stable under multiple clustering runs, we get the set of natural communities that we can track over time. We demonstrate that such natural communities allow us to identify emerging communities and track temporal changes in the underlying structure of our network data.