680 research outputs found

    Graph theoretic generalizations of clique: optimization and extensions

    Get PDF
    This dissertation considers graph theoretic generalizations of the maximum clique problem. Models that were originally proposed in social network analysis literature, are investigated from a mathematical programming perspective for the first time. A social network is usually represented by a graph, and cliques were the first models of "tightly knit groups" in social networks, referred to as cohesive subgroups. Cliques are idealized models and their overly restrictive nature motivated the development of clique relaxations that relax different aspects of a clique. Identifying large cohesive subgroups in social networks has traditionally been used in criminal network analysis to study organized crimes such as terrorism, narcotics and money laundering. More recent applications are in clustering and data mining wireless networks, biological networks as well as graph models of databases and the internet. This research has the potential to impact homeland security, bioinformatics, internet research and telecommunication industry among others. The focus of this dissertation is a degree-based relaxation called k-plex. A distance-based relaxation called k-clique and a diameter-based relaxation called k-club are also investigated in this dissertation. We present the first systematic study of the complexity aspects of these problems and application of mathematical programming techniques in solving them. Graph theoretic properties of the models are identified and used in the development of theory and algorithms. Optimization problems associated with the three models are formulated as binary integer programs and the properties of the associated polytopes are investigated. Facets and valid inequalities are identified based on combinatorial arguments. A branch-and-cut framework is designed and implemented to solve the optimization problems exactly. Specialized preprocessing techniques are developed that, in conjunction with the branch-and-cut algorithm, optimally solve the problems on real-life power law graphs, which is a general class of graphs that include social and biological networks. Computational experiments are performed to study the effectiveness of the proposed solution procedures on benchmark instances and real-life instances. The relationship of these models to the classical maximum clique problem is studied, leading to several interesting observations including a new compact integer programming formulation. We also prove new continuous non-linear formulations for the classical maximum independent set problem which maximize continuous functions over the unit hypercube, and characterize its local and global maxima. Finally, clustering and network design extensions of the clique relaxation models are explored

    Novel approaches for solving large-scale optimization problems on graphs

    Get PDF
    This dissertation considers a class of closely related NP-hard otpimization problems on graphs that arise in many important applications, including network-based data mining, analysis of the stock market, social networks, coding theory, fault diagnosis, molecular biology, biochemistry and genomics. In particular, the problems of interest include the classical maximum independent set problem (MISP) and maximum clique problem (MCP), their vertex-weighted vesrions, as well as novel optimization models that can be viewed as practical relaxations of their classical counterparts. The concept of clique has been a popular instrument in analysis of networks, and is, essentially, an idealized model of a “closely connected group”, or a cluster. But, at the same time, the restrictive nature of the definition of clique makes the clique model impractical in many applications. This motivated the development of clique relaxation models that relax different properties of a clique. On the one hand, while still possessing some clique-like properties, clique relaxations are not as “perfect” as cliques; and on the other hand, they do not exhibit the disadvantages associated with a clique. Using clique relaxations allows one to compromise between perfectness and flexibility, between ideality and reality, which is a usual issue that an engineer deals with when applying theoretical knowledge to solve practical problems in industry. The clique relaxation models studied in this dissertation were first proposed in the literature on social network analysis, however they have not been well investigated from a mathematical programming perspective. This dissertation considers new techniques for solving the MWISP and clique relaxation problems and investigates their effectiveness from theoretical and computational perspectives. The main results obtained in this work include (i) developing a scale-reduction approach for MWISP based on the concept of critical set and comparing it theoretically with other approaches; (ii) obtaining theoretical complexity results for clique relaxation problems; (iii) developing algorithms for solving the clique relaxation problems exactly; (iv) carrying out computational experiments to demonstrate the performance of the proposed approaches, and, finally, (v) applying the obtained theoretical results to several real-life problems

    Mining subjectively interesting patterns in rich data

    Get PDF

    Characterizing and Detecting Cohesive Subgroups with Applications to Social and Brain Networks

    Get PDF
    Many complex systems involve entities that interact with each other through various relationships (e.g., people in social systems, neurons in the brain). These entities and interactions are commonly represented using graphs due to several advantages. This dissertation focuses on developing theory and algorithms for novel methods in graph theory and optimization, and their applications to social and brain networks. Specifically, the major contributions of this dissertation are three fold. First, this dissertation aims not only to develop a new clique relaxation model based on a structural metric, clustering coefficient, but also to introduce a novel graph clustering algorithm using this model. Clique relaxations are used in classical models of cohesive subgroups in social network analysis. Clustering coefficient was introduced more recently as a structural feature characterizing small-world networks. Leveraging the similarities between the concepts of cohesive subgroups and small-world networks (i.e., graphs that are highly clustered with small path lengths). The first part of this dissertation introduces a new clique relaxation, α-cluster, defined by enforcing a lower bound α on the clustering coefficient in the corresponding induced subgraph. Two different definitions of the clustering coefficient are considered, namely, the local and global clustering coefficient. Certain structural properties of α-clusters are analyzed, and mathematical optimization models for determining the largest size α-clusters in a network are developed and applied to several real-life social network instances. In addition, a network clustering algorithm based on local α-cluster is introduced and successfully evaluated. Second, this dissertation explores a novel mathematical model called the maximum independent union of cliques problem (max IUC problem), which arises as a special case of α-clusters. It is an interesting problem for which both the maximum clique and maximum independent sets are feasible solutions and individually their corresponding sizes are lower bounds for the size of the IUC solution. After presenting the structural properties as well as the complexity results of different graph types (planar, unit disk graphs and claw-free graphs), an integer programming formulation is developed, followed by a branch-and-bound algorithm and several heuristic methods to approximate the maximum independent union of cliques problem. The developed methods have been empirically evaluated on many benchmark instances. Finally, this dissertation, in collaboration with Texas Institute of Preclinical Studies (TIPS), applies clique relaxation models to explore a new experimental data to understand the effect of concussion on animal brains. Our research involves cohesive and robust clustering analysis of animal brain networks utilizing a unique and novel experimental data. In collaboration with TIPS, we have analyzed multiple pairs of fMRI data about animal brains that are measured before and after a concussion. We utilize network analysis to first identify the similar regions in animal brains, and then compare how these regions as well as graph structural properties change before and after a concussion. To the best of our knowledge, this study is unique in the literature in that it not only explicitly examines the relation between concussion level and the functional unit interaction but also uses very detailed and fine-grained fMRI measurements of brain data

    Extraction and Analysis of Facebook Friendship Relations

    Get PDF
    Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms
    • …
    corecore