2013
DOI: 10.1007/978-3-642-37210-0_40
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Intruder or Welcome Friend: Inferring Group Membership in Online Social Networks

Abstract: Abstract. Inferring Online Social Networks (OSN) group members may help to evaluate the authenticity of an applicant asking to join a certain group, and secure vulnerable populations online, such as children. We propose machine learning based methods, which associate OSN members' affiliation with virtual groups based on personal, topological, and group affiliation features. The study applies and evaluates the methods empirically, on two social networks (Ning and TheMarker). The experimental results demonstrate… Show more

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Cited by 14 publications
(4 citation statements)
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“…In this paper, we have used three datasets [10] and one dataset in [33]. Each dataset has both one mode and two mode data which are preprocessed to give the edges (both simple and hyperedges) of the graph on which the experiments are performed.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…In this paper, we have used three datasets [10] and one dataset in [33]. Each dataset has both one mode and two mode data which are preprocessed to give the edges (both simple and hyperedges) of the graph on which the experiments are performed.…”
Section: Methodsmentioning
confidence: 99%
“…The first three datasets are a Facebook-like Forum network (FF), Newmanąŕs Scientific Collaboration network (NSC) and Norwegian Interlocking Directorate (NID) [10]. The fourth dataset is TheMarker [33].…”
Section: Methodsmentioning
confidence: 99%
“…Some SN analytics can help identify socialbots that penetrate the organization's social cycle. 15 However, similar to Sybil defense, these methods are effective mainly against unsophisticated attackers. Here, we investigate methods for trapping socialbots that establish a foothold in the organization using more sophisticated strategies such as attk_pa, attk_pacf, and attk_opt.…”
Section: Detecting Advanced Socialbotsmentioning
confidence: 99%
“…The first one is based on 16 real-world graphs: Gnutella (five graphs), Bitcoin (two graphs), Autonomous Systems (two graphs), Ning, Escorts, Anybeat, Google Plus, Facebook1, Facebook2, and Douban. They are taken from several online repositories, including the Stanford Large Network Data set Collection (SNAP, [18]), the BGU Social Networks Security Research Group (BGU, [20]), the Koblenz Network Collection (KONECT, [17]), and the Network Repository (N.R., [26]). All graphs are undirected (i.e., any directed graph is converted to an undirected one).…”
Section: Data Setsmentioning
confidence: 99%