- Program Chairs:
- Jafar Adibi,
- Marko Grobelnik,
- Dunja Mladenic,
- Patrick Pantel
The LinkKDD-2005 workshop aims to bring together a diverse group of researchers and industry practitioners to advance the state of the art in link discovery. Recently, there has been increasing interest in developing information technology for Link Discovery (LD). LD research studies and develops data mining techniques for extracting valuable patterns linking together seemingly unrelated items. LD, rooted in fields such as discreet mathematics, graph theory, social science, pattern analysis, link analysis and spatial databases, is relevant to a wide range of research topics that have been developed in past decades. Successful LD systems will discover the hidden structure of organizations, relate groups, identify fraudulent behaviour, model group activity and provide early detection of emerging threats. The broader context of this workshop invites both theoretical and applied contributions to LD spanning techniques from Data Mining, Machine Learning, Information Retrieval, Natural Language Processing, Social Networks Analysis, and general Graph Theory.Typical characteristics of link discovery problems are:.. Data is heterogeneous, arising from multiple sources;.. Data and patterns sought include representations of people, organizations, objects, actions and events, each of which has its own set of attributes, and particular types of relations linking them;.. The structure may include temporal, spatial, organizational, and/or transactional patterns;.. A relatively low number of observations for each entity can be recorded and the overall sample is typically small relative to the size of the population;.. The data becomes available over time, so the timing of when to make a decision based on the analysis is a central issue.LD problems are found in various areas such as homeland security, social network analysis, fraud detection, recommendation systems, and user modelling. The interdisciplinary nature of link discovery promotes a concerted effort from various researchers. The purpose of this workshop is to provide a forum to foster such interactions, discuss the new achievements and identify future research directions in link discovery.
Proceeding Downloads
Bayes net graphs to understand co-authorship networks?
Improvements in data collection and the birth of online communities made it possible to obtain very large social networks (graphs). Several communities have been involved in modeling and analyzing these graphs. Usage of graphical models, such as ...
EventRank: a framework for ranking time-varying networks
Node-ranking algorithms for (social) networks do not respect the sequence of events from which the network is constructed, but rather measure rank on the aggregation of all data. For data sets that relate to the flow of information (e.g., email), this ...
Graph building as a mining activity: finding links in the small
Many analysis of data proceed by building a graph out of the data set and then using social network theory and similar tools on the result. However, there is no theory concerning the construction of the graph itself, even though this is a very important ...
Tuning representations of dynamic network data
A dynamic network is a special type of network which is comprised of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing ...
Group and topic discovery from relations and text
We present a probabilistic generative model of entity relationships and textual attributes that simultaneously discovers groups among the entities and topics among the corresponding text. Block-models of relationship data have been studied in social ...
The political blogosphere and the 2004 U.S. election: divided they blog
In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. ...
Capital and benefit in social networks
Recently there has been a surge of interest in social networks. Email traffic, disease transmission, and criminal activity can all be modeled as social networks. In this paper, we introduce a particular form of social network which we call a friendship-...
Email alias detection using social network analysis
This research addresses the problem of correctly relating aliases that belong to the same entity. Previous approaches focused on natural language processing and structured data, whereas in this research we analyze the local association, or "social" ...
Mining hidden community in heterogeneous social networks
Social network analysis has attracted much attention in recent years. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only one kind of relation in the ...
GiveALink: mining a semantic network of bookmarks for web search and recommendation
GiveALink is a public site where users donate their bookmarks to the Web community. Bookmarks are analyzed to build a new generation of Web mining techniques and new ways to search, recommend, surf, personalize and visualize the Web. We present a ...
Discovering important nodes through graph entropy the case of Enron email database
A major problem in social network analysis and link discovery is the discovery of hidden organizational structure and selection of interesting influential members based on low-level, incomplete and noisy evidence data. To address such a challenge, we ...
A latent mixed membership model for relational data
Modeling relational data is an important problem for modern data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to ...
Discovering missing links in Wikipedia
In this paper we address the problem of discovering missing hypertext links in Wikipedia. The method we propose consists of two steps: first, we compute a cluster of highly similar pages around a given page, and then we identify candidate links from ...
Cited By
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Hussain D (2010). Investigation of Key-Player Problem in Terrorist Networks Using Bayes Conditional Probability Handbook of Social Network Technologies and Applications, 10.1007/978-1-4419-7142-5_24, (523-547),
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Getoor L and Diehl C (2005). Link mining, ACM SIGKDD Explorations Newsletter, 10.1145/1117454.1117456, 7:2, (3-12), Online publication date: 1-Dec-2005.
- Proceedings of the 3rd international workshop on Link discovery