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Abstract of Paper from ASONAM 2012, FOSINT-SI 2012, HI-BI-BI 2012 and All Workshops 1 Abstract of Paper from ASONAM 2012, FOSINT-SI 2012, HI-BI-BI 2012 and all workshops Large Social Networks Can Be Targeted for Viral Marketing with Small Seed Sets Paulo Shakarian, Damon Paulo In a "tipping" model, each node in a social network, representing an individual, adopts a behavior if a certain number of his incoming neighbors previously held that property. A key problem for viral marketers is to determine an initial "seed" set in a network such that if given a property then the entire network adopts the behavior. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real- world networks, we found that our approach often finds such sets that are several orders of magnitude smaller than the population size. Our approach also scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed sets in under 3.6 hours. We also find that highly clustered local neighborhoods and dense network-wide community structure together suppress the ability of a trend to spread under the tipping model. The Trilemma of Network Analysis Isadora Dorn, Andreas Lindenblatt, Katharina A. Zweig The recent interest in network analysis is caused by the unprecedented accessibility to large datasets: there are huge, publicly available databases on protein-protein-interactions, air transportation, and street maps which easily lend themselves to a network representation. Once a network is created, all types of path-based network analytic measures can be easily applied: typical examples are centrality measures, but also some clustering algorithms and robustness analysis rely on path-based measures. Borgatti has claimed that centrality measures basically simulate dissemination processes of goods which use a certain subset of paths on the given network [1]; they can thus only be used to describe processes which rely on the same type of good and the same subset of paths. Later, Butts pointed out that the results of a chosen network analytic method strongly vary with modeling decisions taken when turning raw data into networks [2]. In this article we combine these two insights to the trilemma of network analysis which states that the network process of interest, the network representation, and the network analytic method cannot be chosen independently. We discuss on two real-world examples in the realm of air transportation networks how to choose a distance based measure with respect to the context of the data, re-computing similar analyses by Guimer´a et al. [3] and Dall’Asta et al. [4]. In both cases, the path-based measures matching the network process of interest change the interpretation of the previous findings, which shows the potential in regarding the trilemma of network analysis. 2 Abstract of Paper from ASONAM 2012, FOSINT-SI 2012, HI-BI-BI 2012 and all workshops Link and Node Analysis of Gender Based Collaborations in Turkish Social Sciences Bülent Özel This paper examines impact of gender both on publication productivity and on patterns of scientific collaborations in social sciences in Turkey. Bibliographic data on local publications in Turkey is used. It consists of 7835 papers written by 6738 scientists. Findings suggest that (1) there are gender differences at publication productivity, participation, presence and contribution, that (2) there are significantly different tendencies at keeping established co-authorship ties for inter-gender and intra-gender pairs, that (3) there are significant gender differences at positions of individuals in the network structure. It is seen that while female scientists are seen to be embedded in cliques more often than males, males are more active at bridging different components in the network. This study exemplifies an integrated approach to better examine role of gender in scientific collaborations. Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter Ozer Ozdikis, Pinar Senkul, Halit Oguztuzun This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as Word Net, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy. 3 Abstract of Paper from ASONAM 2012, FOSINT-SI 2012, HI-BI-BI 2012 and all workshops Social-Based Conceptual Links: Conceptual Analysis Applied to Social Networks Erick Stattner, Martine Collard In this work, we propose a novel approach for the discovery of frequent patterns in a social network on the basis of both vertex attributes and link frequency. With an analogy to the traditional task of mining frequent item sets, we show that the issue addressed can be formulated in terms of a conceptual analysis that elicits conceptual links. A social-based conceptual link is a synthetic representation of a set of links between groups of vertexes that share similar internal properties. We propose a first algorithm that optimizes the search into the concept lattice of conceptual links and extracts maximal frequent conceptual links. We study the performances of our solution and give experimental results obtained on a sample example. Finally we show that the set of conceptual links extracted provides a conceptual view of the social network. Evolutionary Community Detection for Observing Covert Political Elite Cliques Jyi-Shane Liu, Ke-Chih Ning, Wan-Chun Chuang Among many real world applications of social network analysis, political interaction and executive succession show some unique characteristics of dynamic community evolution and raise interesting research challenges. Interactions of political power among community members are mostly subtle and behind the scene. Visible relations are only nominal and are not readily apparent to key findings. Under such difficult circumstances of information deficiency, the research problem is to uncover the inner relations among some of the network entities and to discover the hidden network structure based on these inner relations. In this research, our objective is to identify the inner circles of government political power and bureaucracy underneath formal work relations and observe how the political elite groups form and change over time. A government official job change network in a time span of over twenty years is built to model synchronous post assignment and job promotion within a time window as entity relations. In each snapshot of network evolution, communities that exhibit strong association of synchronous job change are identified by the edge betweenness decomposition algorithm. Then, an event-based framework is used to characterize community behavior patterns in consecutive changes of network structures. The approach is effectually demonstrated on two scenarios: (1) identifying and tracking the inner circle of a leading political figure, (2) finding succession pool members in government agencies. We further propose two evolutionary community variation indexes to assess political executive succession. Experimental results with actual government personnel data provide evidence that government agency succession can be reasonably measured. This work also has the practical value of providing objective scrutiny on political power transition for the benefit of public interest. 4 Abstract of Paper from ASONAM 2012, FOSINT-SI 2012, HI-BI-BI 2012 and all workshops Measuring Topological Robustness of Networks under Sustained Targeted Attacks Mahendra Piraveenan, Shahadat Uddin, Kon Shing Kenneth Chung In this paper, we introduce a measure to analyse the structural robustness of complex networks, which is specifically applicable in scenarios of targeted, sustained attacks. The measure is based on the changing size of the largest component as the network goes through disintegration. We argue that the measure can be used to quantify and compare the effectiveness of various attack strategies. Applying this measure, we confirm the result that scale-free networks are comparatively less vulnerable to random attacks and more vulnerable to targeted attacks. Then we analyse the robustness of a range of real world networks, and show that most real world networks are least robust to attacks based on betweenness of nodes. We also show that the robustness of some networks are more sensitive to the attack strategy compared to others, and given the disparity in the computational complexities of calculating various centrality measures, the robustness coefficient introduced can play a key role in choosing the attack and defence strategies for real world networks. While the measure is applicable to all types of complex networks, we clearly demonstrate its relevance to social network analysis. Analyzing User Retweet Behavior on Twitter Zhiheng
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