SybilSCAR: Sybil detection in online social networks via local rule based propagation

By Binghui Wang, Le Zhang, Neil Zhenqiang Gong. 2017

107 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Jinyuan Jia
4. Hao Fu
5. Le Zhang
6. Prateek Mittal
7. Kurt Thomas
8. Peng Gao

In Computer Networks and Communications

1. Binghui Wang
2. Neil Zhenqiang Gong
3. Jinyuan Jia
4. Le Zhang
5. Hao Fu
6. Prateek Mittal
7. Peng Gao
8. Sanjeev R. Kulkarni

In Information Systems

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Jinyuan Jia
4. Le Zhang
5. Hao Fu
6. Peng Gao
7. Sanjeev R. Kulkarni
8. Kurt Thomas

Citations in the corpus

• Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “For instance, certain state-of-the-art collective classification methods assign the same weight to all edges and use Linearized Loopy Belief Propagation (LinLBP) to propagate the reputation scores [62,64,65].”
• SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, methodology): “Sybil ranking performance: Following the established convention [14], [16], [17], [19], we evaluate the ranking performance of the compared approaches by ranking all nodes in an ascending order of their final scores.”
• Random Walk Based Fake Account Detection in Online Social Networks (2017, methodology): “[17] proposed SybilSCAR, a general framework to unify random walk based methods and MRF based methods.”
• GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (2017, methodology): “Therefore, for the Twitter dataset, we obtained the AUC results in Table III for SSL, SybilBelief, SybilSCAR, and GANG using the sampled balanced training dataset; and we obtained the results in Figure 2 using the original unbalanced training dataset.”
• SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, methodology): “[30,29] proposed a local rule based framework to unify random walk and LBP based approaches.”
• Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, methodology): “State-of-the-art collective classification methods [2], [5], [6], [11], [13]–[15], [17]–[20], [22]–[29] have three key steps.”