Being able to identify the groups of clients that are responsible for a significant portion of a Web site's requests can be helpful to both the Web site and the clients. In a Web application, it is beneficial to move content closer to groups of clients that are responsible for large subsets of requests to an origin server. We introduce clusters---a grouping of clients that are close together topologically and likely to be under common administrative control. We identify clusters using a ``network-aware" method, based on information available from BGP routing table snapshots.

On Network-Aware Clustering of Web Clients

Towards felicitous decision making

Many behavioral phenomena have been found to spread interpersonally through social networks, in a manner similar to infectious diseases. An important difference between social contagion and traditional infectious diseases, however, is that behavioral phenomena can be acquired by non-social mechanisms as well as through social transmission. We introduce a novel theoretical framework for studying these phenomena (the SISa model) by adapting a classic disease model to include the possibility for ‘automatic’ (or ‘spontaneous’) non-social infection. We provide an example of the use of this framework by examining the spread of obesity in the Framingham Heart Study Network. The interaction assumptions of the model are validated using longitudinal network transmission data. We find that the current rate of becoming obese is 2 per year and increases by 0.5 percentage points for each obese social contact. The rate of recovering from obesity is 4 per year, and does not depend on the number of non-obese contacts. The model predicts a long-term obesity prevalence of approximately 42, and can be used to evaluate the effect of different interventions on steady-state obesity. Model predictions quantitatively reproduce the actual historical time course for the prevalence of obesity. We find that since the 1970s, the rate of recovery from obesity has remained relatively constant, while the rates of both spontaneous infection and transmission have steadily increased over time. This suggests that the obesity epidemic may be driven by increasing rates of becoming obese, both spontaneously and transmissively, rather than by decreasing rates of losing weight. A key feature of the SISa model is its ability to characterize the relative importance of social transmission by quantitatively comparing rates of spontaneous versus contagious infection. It provides a theoretical framework for studying the interpersonal spread of any state that may also arise spontaneously, such as emotions, behaviors, health states, ideas or diseases with reservoirs.

Infectious Disease Modeling of Social Contagion in Networks

Influence maximization in social networks based on discrete particle swarm optimization

Influence maximization is a classic optimization problem studied in the area of social network analysis and viral marketing. Given a network, it is defined as the problem of finding k seed nodes so that the influence spread of the network can be optimized. Kempe et al. have proved that this problem is NP hard and the objective function is submodular, based on which a greedy algorithm was proposed to give a near-optimal solution. However, this simple greedy algorithm is time consuming, which limits its application on large-scale networks. Heuristic algorithms generally cannot provide any performance guarantee. To solve this problem, in this paper we propose CoFIM, a community-based framework for influence maximization on large-scale networks. In our framework the influence propagation process is divided into two phases: (i) seeds expansion; and (ii) intra-community propagation. The first phase is the expansion of seed nodes among different communities at the beginning of diffusion. The second phase is the influence propagation within communities which are independent of each other. Based on the framework, we derive a simple evaluation form of the total influence spread which is submodular and can be efficiently computed. Then we further propose a fast algorithm to select the seed nodes. Experimental results on synthetic and nine real-world large datasets including networks with millions of nodes and hundreds of millions of edges show that our algorithm achieves competitive results in influence spread as compared with state-of-the-art algorithms and it is much more efficient in terms of both time and memory usage.

CoFIM: A community-based framework for influence maximization on large-scale networks

This paper describes a new approach of viewing a social relation as a string with various forces acting on it. Accordingly, a tension measure for a relation is defined. Various component forces of the tension measure are identified based on the structural information of the network. A new variant of rough set, namely, double bounded rough set, is developed in order to define these forces mathematically. It is revealed experimentally with synthetic and real-world data that positive and negative tension characterizes, relatively, the presence and absence of a physical link between two nodes. An algorithm based on tension measure is proposed for link prediction. Superiority of the algorithm is demonstrated on nine real-world networks, which include four temporal networks. The source code for calculating tension measure and link prediction algorithm is publicly available at https://gitlab.com/suman5/social-tension-measure .

Double Bounded Rough Set, Tension Measure, and Social Link Prediction

Application of Machine Learning in the Social Network

A high percentage of information that propagates through a social network is sourced from different exogenous sources. E.g., individuals may form their opinions about products based on their own experience or reading a product review, and then share that with their social network. This sharing then diffuses through the network, evolving as a combination of both network and external effects. Besides, different individuals (nodes in a social network) have different degrees of exposition to their external sources, as well. Modeling this influence of external sources is important in order to understand the diffusion process and predict future content sharing patterns. Recognizing this fusion of intrinsic (network) effect and exogenous (external) effect, this paper develops a novel fuzzy relative willingness (FRW) model. Leveraging a fuzzy set approach provides a way to handle the uncertainties arising within the human concept of willingness. We demonstrate that FRW is able to accurately identify both top- $k$ most content producers and diffusion effect based on external influence. We also demonstrate that the fuzzy set theory provides a compelling framework to model uncertainties pertaining to the influence as well as the susceptibility of individuals for both network and exogenous effects.

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Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

Social networks are becoming an integral part of the modern society. Popular social network applications like Facebook, Twitter produces data in huge scale. These data shows all the characteristic of Big data. Accordingly, it leads to a deep change in the way social networks were being analyzed. The chapter describes a model of social network and its applications within the purview of information diffusion and community structure in network analysis. Here fuzzy granulation theory is used to model uncertainties in social networks. This provides a new knowledge representation scheme of relational data by taking care of the indiscernibility among the actors as well as the fuzziness in their relations. Various measures of network are defined on this new model. Within the context of this knowledge framework of social network, algorithms for target set selection and community detection are developed. Here the target sets are determined using the new measure granular degree, whereas it is granular embeddedness, together with granular degree, which is used for detecting various overlapping communities. The resulting community structures have a fuzzy-rough set theoretic description which allows a node to be a member of multiple communities with different memberships of association only if it falls in the (rough upper - rough lower) approximate region. A new index, called normalized fuzzy mutual information is introduced which can be used to quantify the similarity between two fuzzy partition matrices, and hence the quality of the communities detected. Comparative studies demonstrating the superiority of the model over graph theoretic model is shown through extensive experimental results.

Granular Social Network: Model and Applications

Prediction of an imminent catastrophic event in a driven disordered system is of paramount importance - from the laboratory scale controlled fracture experiment to the largest scale of mechanical failure i.e., earthquakes. It has been long conjectured that the statistical regularities in the energy emission time series mirrors the"health"of such driven systems and hence have the potential for forecasting imminent catastrophe. Among other statistical regularities, a measure of how unequal the avalanche sizes are, is potentially a crucial indicator of imminent failure. The inequalities of avalanche sizes are quantified using inequality indices traditionally used in socio-economic systems: the Gini index (g), the Hirsch index (h) and the Kolkata index (k). It is then shown analytically (for mean field) and numerically (for non mean field) in models of quasi-brittle materials that the indices show universal behavior near the breaking points in such models and hence could serve as indicators of imminent breakdown of stressed disordered systems.

Suman Kundu

Papers

Double Bounded Rough Set, Tension Measure, and Social Link Prediction

Application of Machine Learning in the Social Network

Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

Granular Social Network: Model and Applications

Inequality of avalanche sizes in models of fracture