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SocInfo Tables of Contents: 1011121313w14

Proceedings of the 2010 International Conference on Social Informatics

Fullname:SocInfo 2010: Second International Conference on Social Informatics
Editors:Leonard Bolc; Marek Makowski; Adam Wierzbicki
Location:Laxenburg, Austria
Dates:2010-Oct-27 to 2010-Oct-29
Publisher:Springer Berlin Heidelberg
Series:Lecture Notes in Computer Science 6430
Standard No:DOI: 10.1007/978-3-642-16567-2 hcibib: SocInfo10; ISBN: 978-3-642-16566-5 (print), 978-3-642-16567-2 (online)
Papers:17
Pages:249
Links:Online Proceedings | Conference Website
Case-Based Team Recommendation BIBAKFull-Text 1-18
  Yonata Andrelo Asikin; Michele Brocco; Wolfgang Woerndl
Team recommendation is required for composing an appropriate team for a particular task or project by selecting/choosing among the adequate/best team members. Usually project managers decide how to compose a team based on their experience in similar projects. Given this best practice we propose to algorithmically compose appropriate teams for a task by applying case-based reasoning on a previously developed meta-model for team recommendation. We evaluate our approach through comparing the ranking given by a domain expert with the result of our recommender and conclude with a discussion of these results.
Keywords: team recommendation; case-based reasoning
Toward a Behavioral Approach to Privacy for Online Social Networks BIBAFull-Text 19-34
  Lerone D. Banks; S. Felix Wu
We examine the correlation between user interactions and self reported information revelation preferences for users of the popular Online Social Network (OSN), Facebook. Our primary goal is to explore the use of indicators of tie strength to inform localized, per-user privacy preferences for users and their ties within OSNs. We examine the limitations of such an approach and discuss future plans to incorporate this approach into the development of an automated system for helping users define privacy policy. As part of future work, we discuss how to define/expand policy to the entire social network. We also present additional collected data similar to other studies such as perceived tie strength and information revelation preferences for OSN users.
A Meta Model for Team Recommendations BIBAFull-Text 35-50
  Michele Brocco; Georg Groh; Florian Forster
Teams are an important organizational unit and need to be composed appropriately. Whenever a high number of possible team members exists, the complexity of the composition task can not be effectively handled by humans. To support the composition in this scenario, team recommenders can be used. In this paper we discuss and formalize a flexible approach using a generic meta model for implementing various team composition strategies derived from a literature review. In order to demonstrate its use and its compatibility with a generic team recommendation approach, we then translate some of the theoretical team composition approaches found in the literature.
Node Degree Distribution in Affiliation Graphs for Social Network Density Modeling BIBAFull-Text 51-61
  Szymon Chojnacki; Krzysztof Ciesielski; Mieczyslaw Klopotek
The purpose of this article is to link high density in social networks with their underlying bipartite affiliation structure. Density is represented by an average number of a node's neighbors (i.e. node degree or node rank). It is calculated by dividing a number of edges in a graph by a number of vertices. We compare an average node degree in real-life affiliation networks to an average node degree in social networks obtained by projecting an affiliation network onto a user modality. We have found recently that the asymptotic Newmann's explicit formula relating node degree distributions in an affiliation network to the density of a projected graph overestimates the latter value for real-life datasets. We have also observed that this property can be attributed to the local tree-like structure assumption. In this article we propose a procedure to estimate the density of a projected graph by means of a mixture of an exponential and a power-law distributions. We show that our method gives better density estimates than the classic formula.
Achieving Optimal Privacy in Trust-Aware Social Recommender Systems BIBAKFull-Text 62-79
  Nima Dokoohaki; Cihan Kaleli; Huseyin Polat; Mihhail Matskin
Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about trust-aware recommenders such as alleviating profile sparsity or calculation cost efficiency, while least attention has been paid on investigating the notion of privacy surrounding the disclosure of individual ratings and most importantly protection of trust computation across social networks forming the backbone of these systems. To contribute to addressing problem of privacy in trust-aware recommenders, within this paper, first we introduce a framework for enabling privacy-preserving trust-aware recommendation generation. While trust mechanism aims at elevating recommender's accuracy, to preserve privacy, accuracy of the system needs to be decreased. Since within this context, privacy and accuracy are conflicting goals we show that a Pareto set can be found as an optimal setting for both privacy-preserving and trust-enabling mechanisms. We show that this Pareto set, when used as the configuration for measuring the accuracy of base collaborative filtering engine, yields an optimized tradeoff between conflicting goals of privacy and accuracy. We prove this concept along with applicability of our framework by experimenting with accuracy and privacy factors, and we show through experiment how such optimal set can be inferred.
Keywords: Privacy; Trust; Optimization; Data Disguising; Social networks; Collaborative filtering; Recommender systems
First-Use Analysis of Communication in a Social Network BIBAKFull-Text 80-88
  Satoko Itaya; Naoki Yoshinaga; Peter Davis; Rie Tanaka; Taku Konishi; Shinich Doi; Keiji Yamada
The study of communication activity in social networks is aimed at understanding and promoting communications in groups, organizations and communities. In this paper, we propose a method for the analysis of communication records to extract content-based network activity, with a focus on first-use. Links between people in a social network are defined based on content and temporal relation of messages sent and received. We introduce the notion of first-use, first-use paths, and classes of users based on first-usage. First-use is defined with respect to a specific time period and specific communication content. It refers to the sending of messages containing the specified contents for the first time before being receiving them from any other user in the specified time period. First-use paths are defined as sequences of first-use events in communication networks, and m-ary classes of users are defined recursively as users who receive for the first time from (m-1)-ary users. We present an example of application of the analysis to the email records of a large company.
Keywords: social network; information propagation; e-mail analysis
Label-Dependent Feature Extraction in Social Networks for Node Classification BIBAKFull-Text 89-102
  Tomasz Kajdanowicz; Przemyslaw Kazienko; Piotr Doskocz
A new method of feature extraction in the social network for within-network classification is proposed in the paper. The method provides new features calculated by combination of both: network structure information and class labels assigned to nodes. The influence of various features on classification performance has also been studied. The experiments on real-world data have shown that features created owing to the proposed method can lead to significant improvement of classification accuracy.
Keywords: feature extraction; label-dependent features; classification; social network analysis; AMD social network
Computing α-Efficient Cost Allocations for Unbalanced Games BIBAFull-Text 103-112
  Mariusz Kaleta
We consider a network of agents involved in a common project. Resulting project common cost allocation problem can be modeled as a cooperative game with empty core possible. From social point of view, achievement of subsidy-free allocation may play important role, even at a cost of allocation efficiency. Subsidy-free and α-efficient allocation can be obtained by solving linear programme MASIT. However, to find an unique MASIT solution we use notion of equitable rational preference relation and apply column generation technique. We also show, that there are interesting cases of unbalanced games, and for one of them, TP-game, we present numerical results of our approach.
Recommendation Boosted Query Propagation in the Social Network BIBAFull-Text 113-124
  Grzegorz Kukla; Przemyslaw Kazienko; Piotr Bródka; Tomasz Filipowski
Every single company or institution wants to utilize its resources in the most efficient way and one of the most important resources is knowledge. In the paper, a new SocLaKE system is introduced. It exploits the social network existing within the organization together with information about expertise of community members to recommend the best way to get the answer over the chains of acquaintances. The explanation how the system recommends people and experiments on the sample social network are presented as well.
Social Manipulation of Online Recommender Systems BIBAFull-Text 125-139
  Juan Lang; Matt Spear; S. Felix Wu
Online recommender systems are a common target of attack. Existing research has focused on automated manipulation of recommender systems through the creation of shill accounts, and either do not consider attacks by coalitions of real users, downplay the impact of such attacks, or state that such attacks are difficult to impossible to detect. In this study, we examine a recommender system that is part of an online social network, show that users successfully induced other users to manipulate their recommendations, that these manipulations were effective, and that most such manipulations are detectable even when performed by ordinary, non-automated users.
Bicriteria Models for Fair and Efficient Resource Allocation BIBAFull-Text 140-159
  Wlodzimierz Ogryczak
Resource allocation problems are concerned with the allocation of limited resources among competing agents so as to achieve the best system performances. In systems which serve many users, like in networking, there is a need to respect some fairness rules while looking for the overall efficiency. The so-called Max-Min Fairness is widely used to meet these goals. However, allocating the resource to optimize the worst performance may cause a dramatic worsening of the overall system efficiency. Therefore, several other fair allocation schemes are searched and analyzed. In this paper we show how the scalar inequality measures can be consistently used in bicriteria models to search for fair and efficient allocations while taking into account importance weighting of the agents.
Dynamic Context-Sensitive PageRank for Expertise Mining BIBAFull-Text 160-175
  Daniel Schall; Schahram Dustdar
Online tools for collaboration and social platforms have become omnipresent in Web-based environments. Interests and skills of people evolve over time depending in performed activities and joint collaborations. We believe that ranking models for recommending experts or collaboration partners should not only rely on profiles or skill information that need to be manually maintained and updated by the user. In this work we address the problem of expertise mining based on performed interactions between people. We argue that an expertise mining algorithm must consider a person's interest and activity level in a certain collaboration context. Our approach is based on the PageRank algorithm enhanced by techniques to incorporate contextual link information. An approach comprising two steps is presented. First, offline analysis of human interactions considering tagged interaction links and second composition of ranking scores based on preferences. We evaluate our approach using an email interaction network.
Trust-Based Peer Assessment for Virtual Learning Systems BIBAKFull-Text 176-191
  Milorad Tosic; Valentina Nejkovic
In order to deal with new requirements imposed by emerging learning environments following social computing paradigm, we address the problem of assessment of individual student skills, contributions, and activities. Rather then clicking links to launch tools or to view content, such learning environments encourage more of a monitoring mode of operation that is very difficult to sense and record by the software alone. In this paper we propose adoption of peer-assessment approach in order to overcome the obstacle as well as to make the overall solution scalable. We propose a novel method for students' peer-assessment based on trust concept. The overall approach is presented and practical experiments are conducted using developed web service. The grade scores determined by the learning peers/students are statistically proven as highly correlated with those marked by the teachers, indicating that approach proposed in this paper may be adopted as a legitimate assessment method.
Keywords: wiki; education; teaching and learning; peer-assessment; trust
Exponential Ranking: Taking into Account Negative Links BIBAFull-Text 192-202
  Vincent A. Traag; Yurii E. Nesterov; Paul Van Dooren
Networks have attracted a great deal of attention the last decade, and play an important role in various scientific disciplines. Ranking nodes in such networks, based on for example PageRank or eigenvector centrality, remains a hot topic. Not only does this have applications in ranking web pages, it also allows peer-to-peer systems to have effective notions of trust and reputation and enables analyses of various (social) networks. Negative links however, confer distrust or dislike as opposed to positive links, and are usually not taken into account. In this paper we propose a ranking method we call exponential ranking, which allows for negative links in the network. We show convergence of the method, and demonstrate that it takes into account negative links effectively.
Social Email: A Framework and Application for More Socially-Aware Communications BIBAFull-Text 203-215
  Thomas Tran; Jeff Rowe; S. Felix Wu
As online social networks (OSN) attempt to mimic real life social networks, we have made progress towards using OSNs to provide us with data to allow for richer and more robust online communications. In this paper, we present a novel approach towards socially-aware email. Currently, email provides too little control to the recipient. Our approach, dubbed SoEmail, provides social context to messages using an OSN's underlying social graph. This not only gives the recipient control over who can message her, but it also provides her with an understanding of where the message originated from, socially. Furthermore, users who do not wish to use the built-in social aspect of SoEmail, can send and receive emails without any changes to their behavior. The modifications we made to the email application to provide this social context are not invasive and can be easily ignored by any existing email client. We built SoEmail on top of an existing framework, known as Davis Social Links, which allows SoEmail to be completely agnostic about the underlying OSN. We created a web-based, standards-based web client for SoEmail using Facebook and Gmail as the underlying systems which has been released for public use and has had a good adoption rate.
Measuring Message Propagation and Social Influence on Twitter.com BIBAFull-Text 216-231
  Shaozhi Ye; S. Felix Wu
Although extensive studies have been conducted on online social networks (OSNs), it is not clear how to characterize information propagation and social influence, two types of important but not well defined social behavior. This paper presents a measurement study of 58M messages collected from 700K users on Twitter.com, a popular social medium. We analyze the propagation patterns of general messages and show how breaking news (Michael Jackson's death) spread through Twitter. Furthermore, we evaluate different social influences by examining their stabilities, assessments, and correlations. This paper addresses the complications as well as challenges we encounter when measuring message propagation and social influence on OSNs. We believe that our results here provide valuable insights for future OSN research.
SocialWiki: Bring Order to Wiki Systems with Social Context BIBAKFull-Text 232-247
  Haifeng Zhao; Shaozhi Ye; Prantik Bhattacharyya; Jeff Rowe; Ken Gribble; S. Felix Wu
A huge amount of administrative effort is required for large wiki systems to produce and maintain high quality pages with existing naive access control policies. This paper introduces SocialWiki, a prototype wiki system which leverages the power of social networks to automatically manage reputation and trust for wiki users based on the content they contribute and the ratings they receive. SocialWiki also utilizes interests to facilitate collaborative editing. Although a wiki page is visible to everyone, it can only be edited by a group of users who share similar interests and have a certain level of trust with each other. The editing privilege is circulated among these users to prevent/reduce vandalisms and spams, and to encourage user participation by adding social context to the revision process of a wiki page. By presenting the design and implementation of this proof-of-concept system, we show that social context can be used to build an efficient, self-adaptive and robust collaborative editing system.
Keywords: wiki; collaborative editing; social context modeling; trust management; social network applications