Linking Multiple Identities in Online Social Networks Based on Co-Occurrence Analysis

2015 ◽  
Vol 713-715 ◽  
pp. 2257-2260
Author(s):  
Dong Liu ◽  
Quan Yuan Wu ◽  
Wei Hong Han
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Sliding Window ◽  
Multiple Identities ◽  
Fine Grained ◽  
Series Of Experiments ◽  
Colloquial Style

When tackling the problem of mining multiple fake identities which are controlled by the same individual in internet, traditional techniques used to analyze the posted comments using text-based methods. However, these texts are always in colloquial style which make the effect may not be as obvious as expected. In this paper a new multiple identities linking algorithm is proposed based on the fine-grained analysis of co-occurrence degree of user accounts using sliding window model. Finally, a series of experiments show the effectiveness of our proposed method.

Incremental Group-Level Popularity Prediction in Online Social Networks

2022 ◽  
Vol 22 (1) ◽  
pp. 1-26
Author(s):  
Jingjing Wang ◽  
Wenjun Jiang ◽  
Kenli Li ◽  
Guojun Wang ◽  
Keqin Li
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Low Cost ◽  
Tensor Decomposition ◽  
Group Level ◽  
Fine Grained ◽  
Cp Decomposition ◽  
Web Contents ◽  
Popularity Prediction ◽  
Novel Model

Predicting the popularity of web contents in online social networks is essential for many applications. However, existing works are usually under non-incremental settings. In other words, they have to rebuild models from scratch when new data occurs, which are inefficient in big data environments. It leads to an urgent need for incremental prediction, which can update previous results with new data and conduct prediction incrementally. Moreover, the promising direction of group-level popularity prediction has not been well treated, which explores fine-grained information while keeping a low cost. To this end, we identify the problem of incremental group-level popularity prediction, and propose a novel model IGPP to address it. We first predict the group-level popularity incrementally by exploiting the incremental CANDECOMP/PARAFCAC (CP) tensor decomposition algorithm. Then, to reduce the cumulative error by incremental prediction, we propose three strategies to restart the CP decomposition. To the best of our knowledge, this is the first work that identifies and solves the problem of incremental group-level popularity prediction. Extensive experimental results show significant improvements of the IGPP method over other works both in the prediction accuracy and the efficiency.

scholarly journals Social Networks in which Users are not Small Circles

10.28945/2196 ◽  
2015 ◽  
Vol 18 ◽  
pp. 205-224 ◽  
Author(s):  
Sabah Al-Fedaghi ◽  
Heba AlMeshari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Distribution Systems ◽  
User Behavior ◽  
Clustering Coefficient ◽  
Graph Representations ◽  
Small Circle ◽  
Fine Grained ◽  
Social Network Structure ◽  
Placement Policies

Understanding of social network structure and user behavior has important implications for site design, applications (e.g., ad placement policies), accurate modeling for social studies, and design of next-generation infrastructure and content distribution systems. Currently, characterizations of social networks have been dominated by topological studies in which graph representations are analyzed in terms of connectivity using techniques such as degree distribution, diameter, average degree, clustering coefficient, average path length, and cycles. The problem is that these parameters are not completely satisfactory in the sense that they cannot account for individual events and have only limited use, since one can produce a set of synthetic graphs that have the exact same metrics or statistics but exhibit fundamentally different connectivity structures. In such an approach, a node drawn as a small circle represents an individual. A small circle reflects a black box model in which the interior of the node is blocked from view. This paper focuses on the node level by considering the structural interiority of a node to provide a more fine-grained understanding of social networks. Node interiors are modeled by use of six generic stages: creation, release, transfer, arrival, acceptance, and processing of the artifacts that flow among and within nodes. The resulting description portrays nodes as comprising mostly creators (e.g., of data), receivers/senders (e.g., bus boys), and processors (re-formatters). Two sample online social networks are analyzed according to these features of nodes. This examination points to the viability of the representational method for characterization of social networks.

scholarly journals Privacy-Preserving Assessment of Social Network Data Trustworthiness

2014 ◽  
Vol 23 (02) ◽  
pp. 1441004 ◽  
Author(s):  
Chenyun Dai ◽  
Fang-Yu Rao ◽  
Traian Marius Truta ◽  
Elisa Bertino
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Network Data ◽  
Challenging Problem ◽  
Privacy Concerns ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Series Of Experiments ◽  
Data Trustworthiness

Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

Analysis of User Interactions in Online Social Networks

2011 ◽  
Author(s):  
Seokchan Yun ◽  
Heungseok Do ◽  
Jinuk Jung ◽  
Song Mina ◽  
Namgoong Hyun ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Interactions
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