Link prediction is considered as one of the key tasks in various data mining applications for recommendation systems, bioinformatics, security and worldwide web. The majority of previous works in link prediction mainly focus on the homogeneous networks which only consider one type of node and link. However, real-world networks have heterogeneous interactions and complicated dynamic structure, which make link prediction a more challenging task. In this paper, we have studied the problem of link prediction in the dynamic, undirected, weighted/ unweighted, heterogeneous social networks which are composed of multiple types of nodes and links that change over time. We propose a novel method, called Multivariate Time Series Link Prediction for evolving heterogeneous networks that incorporate (1) temporal evolution of the network; (2) correlations between link evolution and multi-typed relationships; (3) local and global similarity measures; and (4) node connectivity information. Our proposed method and the previously proposed time series methods are evaluated experimentally on a real-world bibliographic network (DBLP) and a social bookmarking network (Delicious). Experimental results show that the proposed method outperforms the previous methods in terms of AUC measures in di®erent test cases.

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Attractiveness of social network analysis as a research topic in many different disciplines is growing in parallel to the continuous growth of the Internet, which allows people to share and collaborate more. Nowadays, detection of community structures, which may be established on social networks, is a popular topic in Computer Science. High computational costs and non-scalability on large-scale social networks are the biggest drawbacks of popular community detection methods. The main aim of this study is to reduce the original network graph to a maintainable size so that computational costs decrease without loss of solution quality, thus increasing scalability on such networks. In this study, we focus on Ant Colony Optimization techniques to find quasicliques in the network and assign these quasi-cliques as nodes in a reduced graph to use with community detection algorithms. Experiments are performed on commonly used social networks with the addition of several large-scale networks. Based on the experimental results on various sized social networks, we may say that the execution times of the community detection methods are decreased while the overall quality of the solution is preserved.

Clustering methods provide users with methods to summarize and organize the huge amount of data in order to help them find what they are looking for. However, one of the drawbacks of clustering algorithms is that the result may vary greatly when using different clustering criteria. In this paper, we present a new clustering algorithm based on graph partitioning approach that only considers the pairwise similarities. The algorithm makes no assumptions about the size or the number of clusters. Besides this, the algorithm can make use of multiple clustering criteria functions. We will present experimental results on a synthetic data set and a real world web log data. Our experiments indicate that our clustering algorithm can efficiently cluster data items without any constraints on the number of clusters.