Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the WWW, a collection of linked web pages. Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. This is an exciting, rapidly expanding area. In this article, we review some of the common emerging themes. 1.

In a time of information glut, observations about complex systems and phenomena of interest are available in several applications areas, such as biology and text. As a consequence, scientists have started searching for patterns that involve interactions among the objects of analysis, to the effect that research on models and algorithms for network analysis has become a central theme for knowledge discovery and data mining (KDD). The intuitions behind the plethora of approaches rely upon few basic types of networks, identified by specific local and global topological properties, which we term “pure ” topology types. In this paper, (1) we survey pure topology types along with existing sampling algorithms that generate them, (2) we introduce novel algorithms that enhance the diversity of samples, and address the case of cellular topologies, (3) we perform statistical studies of the stability of the properties of pure types to alternative generative algorithms, and a joint study of the separability of pure types, in terms of their embedding in a space of metrics for network analysis, widely adopted in the social and physical sciences. We conclude with a word of caution to the practitioners, who sample pure topology types to assess the “statistical significance” of their findings, e.g., the p-value of the clustering coefficient is sensitive to the sampling algorithm used. We find that different pure types share similar topological properties. Further, real world networks hardly present the variability profile of a single pure type. We suggest the assumption of “mixtures of types ” as an alternative starting point for developing models and algorithms for network analysis.

This paper suggests that the effects of counterterrorism policy on Violent Non-State Actors can be explored via systems dynamics. Specifically, we create a descriptive model of the flow of persons through the terrorism field. We then demonstrate how the model can be extended to explore “what if” analyses of counterterrorism actions, assisting analysts with identifying fallacious logic chains or important knowledge gaps related to causal explanations. Our model instantiations suggest that developing and implementing counterterrorism policy requires continuous and active management to avoid undesirable long term outcomes. 1.

• The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation or the U.S. government. We are grateful to Brian Hirschman from CMU for discussing the multi-agent simulation with us.Keywords: Social network change detection, statistical process control, multi-agent simulation, military, organizational behavior, networks, network statistics The performance of social network change detection (SNCD) is evaluated using a multiagent simulation of company level U.S Army Infantry organizations. Agent interaction is probabilistic, with increased likelihood of communication based on similarity in skills, role, sub-unit of assignment, military rank, and general personality homophily. Various social network measures are monitored for change over time with a Cumulative Sum (CUSUM) control chart, an Exponentially Weighted Moving Average (EWMA), a scan statistic, and a Hamming Distance. Findings show that the average betweenness, the average closeness, and the standard deviation of eigenvector centrality are social network measures that are well-suited for SNCD. This research further supports the efficacy of

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Methods for generating a random sample of networks with desired properties are important tools for the analysis of social, biological, and information networks. Algorithm-based approaches to sampling networks have received a great deal of attention in the recent literature. Most of these algorithms are based on simple intuitions that suggestively associate quantitative topological properties of observed networks with simple generative algorithms and the connectivity patterns they entail. Substantive conclusions in many applications crucially depend on this association holding true. However, a full characterization of this association and the extent to which it holds are not yet available. In this paper, we use data mining techniques to explore the association between a number of algorithms for sampling networks, and the full gamut of quantitative topological properties of the networks generated by those algorithms. Of interest is whether and to what extent the algorithms that are meant to generate different types of networks lead to networks with different topological properties, and whether alternative algorithms that are meant to generate a specific type of network lead to networks with the same or similar topological properties. We find that different network sampling algorithms can yield networks with similar topological properties. We also find that the alternative algorithms for a specific connectivity pattern can yield networks with different topological properties. Conclusions based on simulated network studies must focus on topological properties of a network instead of the claimed network types. This fact has important implications for net-