Random Walk Based Fake Account Detection in Online Social Networks

By Jinyuan Jia, Binghui Wang, Neil Zhenqiang Gong. 2017

136 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): “Moreover, our attacks can also transfer to other collective classification methods including random walk based meth-ods [34], Loopy Belief Propagation based methods [23], and recent Joint Weight Learning and Propagation method [63] that learns edge weights, as well as graph neural network methods…”
• GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (2017, methodology): “On the transformed undirected Twitter graph with bidirectional edges only, SybilWalk achieves performance close to SybilBelief.”
• SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, methodology): “For instance, random walk based approaches include SybilGuard [11], Sybil-Limit [12], SybilInfer [13], SybilRank [14], CIA [15], and SybilWalk [23].”
• SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, background): “One category of Sybil detection approaches leverage the global structure of the social network [39,38,8,26,37,7,33,36,6,14,9,11,30,18,28,29].”
• Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, methodology): “RW-B [3], [19], [45] leverages both labeled positive nodes and labeled negative nodes to assign prior reputation scores, e.g., labeled positive nodes, labeled negative nodes, and unlabeled nodes have prior reputation scores of 1, -1, and 0, respectively.”
• Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation (2018, methodology): “RW-based methods [8], [9], [10], [11], [12], [13], [14], [15], [16] suffer from one or two major limitations: 1) they can only leverage either labeled benign users or labeled Sybils in the training dataset, but not both, which limits their detection accuracies; and 2) they are not robust to label no”
• SpotFake: A Multi-modal Framework for Fake News Detection (2019, methodology): “Various approaches to detect fake accounts include the use of cluster formation [9], random walks [10], steganalysis technique [11] and entropy minimization discretization [12].”