Christos Faloutsos

Automated Assistance for Eliciting User Expectations

2018 · KiltHub Repository

People often use software for mundane tasks and expect it to be dependable enough for their needs. Unfortunately, the incomplete and imprecise specifications of such everyday software inhibit many dependability enhancement techniques because these require …

BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

2016

Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or purchase fake reviews to manipulate perception of their products and services. Fake reviews are often detected based on several signs, …

FairJudge: Trustworthy User Prediction in Rating Platforms

2017 · arXiv (Cornell University)

Rating platforms enable large-scale collection of user opinion about items (products, other users, etc.). However, many untrustworthy users give fraudulent ratings for excessive monetary gains. In the paper, we present FairJudge, a system to identify …

OEC: Open-Ended Classification for Future-Proof Link-Fraud Detection.

2017 · arXiv (Cornell University)

When tasked to find fraudulent social network users, what is a practitioner to do? Traditional classification can lead to poor generalization and high misclassification given few and possibly biased labels. We tackle this problem by …

EvoKG

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from …

Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining

2023

How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is …

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

2024 · arXiv (Cornell University)

Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously. One solution is to use …