Correlation between clustering and degree in affiliation networks

We are interested in the probability that two randomly selected neighbors of a random vertex of degree (at least) $k$ are adjacent. We evaluate this probability for a power law random intersection graph, where each vertex is prescribed a collection of attributes and two vertices are adjacent whenever they share a common attribute. We show that the probability obeys the scaling $k^{-\delta}$ as $k\to+\infty$. Our results are mathematically rigorous. The parameter $0\le \delta\le 1$ is determined by the tail indices of power law random weights defining the links between vertices and attributes

Rise and decline process of online communities : modeling social balance of participants

Some online communities like Friendster had declined, and some of the others are said to be declining. Recent research has revealed the mechanism of decline as well as that of rise in each community. However, no comprehensive research has yet revealed the difference in declining mechanisms of each communities. We considered the online communities as networks of users and topics and defined behavior of users using Heider's balance theory. Users in our model are in a dilemma, stuck between topic preference and the balance between neighboring users. How the user behaves in the dilemma, his/her strategy, disseminates to other users. We simulate online communities using the model and observe the rise and decline of different kinds of communities. As a result, we found that two types of communities tend to develop with many users: communities in which the topic changes dynamically (FreeTopic-type) and communities in which the topic changes gradually (Topic-type). However, the property of each community and behavior of users are different. We found by simulation that the collaborative behavior of users happens very frequently in the FreeTopictype community, in which users consider the balance between each other rather than their topic preference. As a result, the FreeTopic-type communities do not often crash (i.e. quickly lose users). In addition, we confirmed that the postings about a topic are either negative or positive in the FreeTopic-type community. On the other hand, in the Topic-type community, simulation results indicate that users prioritize their preference for a topic. This causes the community to crash very frequently. However, users in such a community are found to obtain more benefits than in FreeTopic-type communities. It can be said that, after crashes occur, the community is still relatively beneficial for some users who remain

S3G2: a Scalable Structure-correlated Social Graph Generator

Benchmarking graph-oriented database workloads and graph-oriented database systems are increasingly becoming relevant in analytical Big Data tasks, such as social network analysis. In graph data, structure is not mainly found inside the nodes, but especially in the way nodes happen to be connected, i.e. structural correlations. Because such structural correlations determine join fan-outs experienced by graph analysis algorithms and graph query executors, they are an essential, yet typically neglected, ingredient of synthetic graph generators. To address this, we present S3G2: a Scalable Structure-correlated Social Graph Generator. This graph generator creates a synthetic social graph, containing non-uniform value distributions and structural correlations, and is intended as a testbed for scalable graph analysis algorithms and graph database systems. We generalize the problem to decompose correlated graph generation in multiple passes that each focus on one so-called "correlation dimension"; each of which can be mapped to a MapReduce task. We show that using S3G2 can generate social graphs that (i) share well-known graph connectivity characteristics typically found in real social graphs (ii) contain certain plausible structural correlations that influence the performance of graph analysis algorithms and queries, and (iii) can be quickly generated at huge sizes on common cluster hardware

S3G2: a Scalable Structure-correlated Social Graph Generator

Benchmarking graph-oriented database workloads and graph-oriented database systems are increasingly becoming relevant in analytical Big Data tasks, such as social network analysis. In graph data, structure is not mainly found inside the nodes, but especially in the way nodes happen to be connected, i.e. structural correlations. Because such structural correlations determine join fan-outs experienced by graph analysis algorithms and graph query executors, they are an essential, yet typically neglected, ingredient of synthetic graph generators. To address this, we present S3G2: a Scalable Structure-correlated Social Graph Generator. This graph generator creates a synthetic social graph, containing non-uniform value distributions and structural correlations, and is intended as a testbed for scalable graph analysis algorithms and graph database systems. We generalize the problem to decompose correlated graph generation in multiple passes that each focus on one so-called "correlation dimension"; each of which can be mapped to a MapReduce task. We show that using S3G2 can generate social graphs that (i) share well-known graph connectivity characteristics typically found in real social graphs (ii) contain certain plausible structural correlations that influence the performance of graph analysis algorithms and queries, and (iii) can be quickly generated at huge sizes on common cluster hardware

Clustering and the hyperbolic geometry of complex networks

Clustering is a fundamental property of complex networks and it is the mathematical expression of a ubiquitous phenomenon that arises in various types of self-organized networks such as biological networks, computer networks or social networks. In this paper, we consider what is called the global clustering coefficient of random graphs on the hyperbolic plane. This model of random graphs was proposed recently by Krioukov et al. as a mathematical model of complex networks, under the fundamental assumption that hyperbolic geometry underlies the structure of these networks. We give a rigorous analysis of clustering and characterize the global clustering coefficient in terms of the parameters of the model. We show how the global clustering coefficient can be tuned by these parameters and we give an explicit formula for this function.Comment: 51 pages, 1 figur

Social network-Based Framework for Users and Web Services Discovery

With the emergence of Web 2.0 and its applications, social networks have facilitated the discovery process of web services, a cornerstone to the development of service computing. Very recently, some frameworks have suggested adding a social element to services' description, discovery, binding and composition. By incorporating the social component in the Service-Oriented Architecture, web services become active entities that can form and be part of social networks. However, merging users and web services in the same social network and analyzing the influence of these entities (i.e., web services and users) on each other have not been examined in the previous proposals and yet to be investigated. In this thesis, we propose a new social network-based framework for analyzing the role and influence of users and web services in the discovery process. We advocate the idea of incorporating, not only social web services, but also social users in the discovery process by merging users and web services nodes in the same global social network. We first discuss the engineering process of such a social network that takes into consideration users and web services characteristics and the types of their interactions. Thereafter, we analyze those types of interactions that fall in one of two categories: web service discovery or user discovery. The goal is to involve social networks of users in the service discovery process and allow web services to be active parts by advertising and introducing themselves to other users. Simulation results show that the proposed approach provides an immediate and wider exposure for web services and makes the discovery easier and efficient

An Evaluation Framework for Business Intelligence Visualization

Nowadays, data visualization is becoming an essential part of data analysis. Business Intelligence Visualization (BIV) is a powerful tool that helps modern business flows faster and smoother than ever before. However, studies on BIV evaluation are severely lacking; most evaluation studies for BIV is guided by general principles of usability, which have limited aspects covered for customers? needs. The purpose of this research is to develop a framework that evaluates BIV, including decision-making experience. First, we did a literature review for good understanding of research progress on related fields, and established a conceptual framework. Second, we performed a user study that implemented this framework with a set of questionnaires to demonstrate how our framework can be used in real business. Our result proved that this framework can catch differences among different designs of BIV from the users? standpoints. This can help design BIV and promote better decision-makings on business affairs