Name
Abstract | ||
|---|---|---|
We consider the problem of designing a robust credit score function in the context of online discussion forums. Credit score function assigns a real-valued credit score to each participant based on activities on the forum. A credit score of a participant quantifies the usefulness of contribution made by her. However, participants can manipulate a credit score function by forming coalitions, i.e., by strategically awarding upvotes, likes, etc. among a subset of agents to maximize their credit scores. We propose a coalition resistant credit score function which discourages such strategic endorsements. We use community detection algorithms to identify close-knit communities in the graph of interactions and characterize coalition identifying community detection metric. In particular, we show that modularity is coalition identifying and provide theoretical guarantees on modularity based credit score function. Finally, we validate our theoretical findings with simulations on illustrative datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3237383.3237404 | PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) |
Field | DocType | Citations |
Graph,Incentive design,Computer science,Credit score,Artificial intelligence,Online discussion,Machine learning,Modularity | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ganesh Ghalme | 1 | 1 | 1.71 |
| Sujit Gujar | 2 | 76 | 25.33 |
| Amleshwar Kumar | 3 | 0 | 0.34 |
| Shweta Jain | 4 | 15 | 2.36 |
| Y. Narahari | 5 | 699 | 98.97 |