Neil Zhenqiang Gong

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Understanding social network structure and evolution has important implications for many aspects of network and system design including provisioning, bootstrapping trust and reputation systems via social networks, and defenses against Sybil attacks. Several recent results suggest that augmenting the social network structure with user attributes (e.g.,(More)
The effects of social influence and homophily suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference. Recently, Yin et al. [28, 29] proposed Social-Attribute Network (SAN), an attribute-augmented social network, to integrate network structure and node attributes to perform(More)
—We study techniques for identifying an anonymous author via linguistic stylometry, i.e., comparing the writing style against a corpus of texts of known authorship. We experimentally demonstrate the effectiveness of our techniques with as many as 100,000 candidate authors. Given the increasing availability of writing samples online, our result has serious(More)
Understanding and modeling the mechanisms by which directed social networks evolve are active areas of research. One related emerging topic is to understand and predict the formation of reciprocal edges, which has many potential applications such as directed social network modeling, friend recommendation, information propagation and targeted spamming.(More)
—The general problem of answering top-k queries can be modeled using lists of objects sorted by their local scores. Fagin et al. proposed the " middleware cost " for a top-k query algorithm, and proposed the efficient sequential Threshold Algorithm (TA). However, since the size of the dataset can be incredible huge, the middleware cost of sequential TA may(More)
Exact top-k query processing has caught much attention recently because of its wide use in many research areas. Since missing the truly best answers is inherent and unavoidable due to the user's subjective judgment, and the cost of processing exact top-k queries is highly expensive for datasets with huge volume, it is intriguing to answer approximate top-k(More)
The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an attribute-augmented social network model, which we call <i>Social-Attribute Network</i> (SAN), to integrate network structure(More)
—In this paper, we conduct the first comprehensive quantification on the perfect de-anonymizability and partial de-anonymizability of real world social networks with seed information in general scenarios, where a social network can follow an arbitrary distribution model. This quantification provides the theoretical foundation for existing structure based(More)
The effects of social influence and network autocorrelation suggest that both network structure and node attribute information should inform the tasks of link prediction and node attribute inference. However , the algorithmic question of how to efficiently incorporate these two sources of information remains largely unanswered. We propose a Social-Attribute(More)
—Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their(More)