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Affiliation Networks

Event Classification and Relationship Labeling in Aliation Networks

February 11, 2019July 6, 2017 by admin

Abstract

Many domains are best described as an affiliation network in which there are entities such as actors, events and organizations linked together in a variety of relationships. Relational classification in these domains requires the collective classification of both entities and relationships. In this paper, we investigate the use of relational Markov networks (RMN) for relational classification in affiliation networks. We study two tasks, event classification and relationship labeling, and discuss general issues in constructing an appropriate RMN from an affiliation network. We evaluate our methods on a novel dataset describing terrorist affiliation networks which includes data about actors (terrorists), events (terrorist attacks) and organizations (terrorist organizations). Our results highlight several important issues concerning the effectiveness of relational classification and our experiments show that the relational structure significantly helps relationship labeling.

1. Introduction
Traditional machine learning techniques mainly concentrate on identically and independently distributed samples. However, most real-world datasets are relational in nature and the correlations due to the link structure provide an important source of information.

The KOJAK Group Finder: Connecting the Dots via Integrated Knowledge-Based and Statistical Reasoning

February 10, 2019July 6, 2017 by admin

Abstract

Link discovery is a new challenge in data mining whose primary concerns are to identify strong links and discover hidden relationships among entities and organizations based on low-level, incomplete and noisy evidence data. To address this challenge, we are developing a hybrid link discovery system called KOJAK that combines state-of-theart knowledge representation and reasoning (KR&R) technology with statistical clustering and analysis techniques from the area of data mining. In this paper we report on the architecture and technology of its first fully completed module called the KOJAK Group Finder. The Group Finder is capable of finding hidden groups and group members in large evidence databases. Our group finding approach addresses a variety of important LD challenges, such as being able to exploit heterogeneous and structurally rich evidence, handling the connectivity curse, noise and corruption as well as the capability to scale up to very large, realistic data sets. The first version of the KOJAK Group Finder has been successfully tested and evaluated on a...

Link Prediction in Affiliation Networks

February 10, 2019July 6, 2017 by admin

Applications of Link Prediction

1) Retail Recommender Systems
Used by e-commerce and marketing companies to recommend potential products to customers.
Amazon.com: recommends books, music and retail products to potential customers.
SkyMall.com: recommends retail products to potential customers.
Netflix.com and MovieLens.org: recommend movies to potential viewers.
[Riedl & Konstan 2002]

Applications of Link Prediction

2) Information Filtering
Used by news forums, academic publication repositories and web search engines.
GroupLens: recommends current event news articles to subscribers.
ReferallWeb: recommends authors and information to people based on a trusted chain of referrals from other users.
CiteSeer: recommends scientific publications to researchers.
[Resnick & Varian 1997]

Applications of Link Prediction

3) Biological Systems – Protein Interactions
Due to the fact that protein-protein interaction networks contain hub proteins and contain triads and tetrads at a higher frequency than random networks, it is possible to predict future protein interactions. “We show that we can leverage the information encoded in consensus interaction patterns to generate high relevance predictions for new

Analyzing Affiliation Networks

February 10, 2019July 6, 2017 by admin

In social network analysis, the term “affiliations” usually refers to membership or participation data, such as when we have data on which actors have participated in which events. Often, the assumption is that comebership in groups or events is an indicator of an underlying social tie. For example, Davis Gardner and mGardner (1941) used data provided by the society pages of a local newspaper to uncover distinct social circles among a set of society women. Similarly, Domhoff (1967) and others have used comembership in corporate boards to search for social elites (e.g., Allen, 1974; Carroll, Fox and Ornstein, 1982; Galaskiewicz, 1985; Westphal and Khanna, 2003). Alternatively, we can see co-participation as providing opportunities for social ties to develop, which in turn provide opportunities things like ideas to flow between actors. For example, Davis (1991; Davis and Greeve, 1997) studied the diffusion of corporate practices such as poison pills and golden parachutes. He finds evidence that poison pills diffuse through chains of interlocking directorates, where board...

Affiliation Networks

February 9, 2019July 6, 2017 by admin

ABSTRACT

In the last decade, structural properties of several naturally arising networks (the Internet, social networks, the web graph, etc.) have been studied intensively with a view to understanding their evolution. In recent empirical work, Leskovec, Kleinberg, and Faloutsos identify two new and surprising properties of the evolution of many real-world networks: densification (the ratio of edges to vertices grows over time), and shrinking diameter (the diameter reduces over time to a constant). These properties run counter to conventional wisdom, and are certainly inconsistent with graph models prior to their work. In this paper, we present the first model that provides a simple, realistic, and mathematically tractable generative model that intrinsically explains all the well-known properties of the social networks, as well as densification and shrinking diameter. Our model is based on ideas studied empirically in the social sciences, primarily on the groundbreaking work of Breiger (1973) on bipartite models of social networks that capture the affiliation of agents to societies....