Mohammed Zuhair Al-Taie

Information Diffusion in Social Networks

In this chapter, we will discuss concepts of information diffusion in social networks. We are interested in knowing how a piece of information (knowledge) is spread through a network. These may be computer viruses spreading on the Internet or a network of computers, diseases through a social network, or rumors and ideas through a social network. Information diffusion methods are commonly used in viral marketing, in collaborative filtering systems, in emergency management, in community detection, and in the study of citation networks.

Understanding expert finding systems: domains and techniques

Expert finding can be required for a variety of purposes: finding referees for a conference paper, recommending consultants for a software project, and identifying qualified answerers for a question in online knowledge-sharing communities, to name a few. This paper presents taxonomy of the task of expert finding that highlights the differences between finding experts, from the type of expertise indicator’s point of view. The taxonomy supports deep understanding of different sources of expertise information in the enterprise or online communities; for example, authored documents, emails, online posts, and social networks. In addition, different content and non-content features that characterize the evidence of expertise are discussed. The goal is to guide researchers who seek to conduct studies regarding the different types of expertise indicators and state-of-the-art techniques for expert finding in organizations or online communities. The paper concludes that although researchers have utilized a large number of graph and machine-learning techniques for locating expertise, there are still technical issues associated with the implementation of some of these methods. It also corroborates that combining content-based expertise indicators and social relationships has the benefit of alleviating some of the issues related to identifying and ranking answer experts. The above findings give implications for developing new techniques for expert finding that can overcome the technical issues associated with the performance of current methods.

Node-Level Analysis

This chapter is concerned with building an understanding of how to do network analysis at the node (ego) level. It shows how to create social networks from scratch, how to import networks, how to find key players in social networks using centrality measures, and how to visualize networks. We will also introduce the important algorithms that are used to gain insights from graphs.

Network-level analysis

In this chapter, we are going to study graphs and networks as a whole, which is different from what we had done in the previous chapters when we analyzed graphs at the node level and the group level. Hence, this chapter addresses concepts such as components and isolates, cores and periphery, network density, shortest paths, reciprocity, affiliation networks and two-mode networks, and homophily.

Group-Level Analysis

In this chapter, we are going to present a number of techniques for detecting cohesive groups in networks such as cliques, clustering coefficient, triadic analysis, structural holes, brokerage, transitivity, hierarchical clustering, and blockmodels. All of which are based on how nodes in a network interconnect. However, among all, cohesion and brokerage types of analysis are two major research topics in social network analysis.

Theoretical Concepts of Network Analysis

Generally speaking, a network is a set of links (ties or edges) and objects (nodes or vertices). These objects could be people, rivers, roads, computers, cities, etc., while links may represent relationships such as friendship, kinship, sexual relationships, the flow of information, etc. Kinds of networks include computer networks, neural networks, semantic networks, food web, supply chain networks, friendship networks, information networks, etc. Network representation borrows some of its notations (e.g., nodes and links) from graph theory and other notations (e.g., the actor-network theory) from social theories.