activity
20162022
most citedKnowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps

32 citations · 41 across the 7 of their papers we have counts for

collaborators

16 papers

cs.LG20213 cited

KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection

Sara Abdali, Neil Shah, Evangelos E. Papalexakis

Graphs are one of the most efficacious structures for representing datapoints and their relations, and they have been largely exploited for different applications. Previously, the…

cs.LG2021

Identifying Misinformation from Website Screenshots

Sara Abdali, Rutuja Gurav, Siddharth Menon +4

Can the look and the feel of a website give information about the trustworthiness of an article? In this paper, we propose to use a promising, yet neglected aspect in detecting the…

cs.LG2020

FairOD: Fairness-aware Outlier Detection

Shubhranshu Shekhar, Neil Shah, Leman Akoglu

Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population. However, when being a minority (as de…

cs.CL20201 cited

The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks

Brihi Joshi, Neil Shah, Francesco Barbieri +1

Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis…

cs.LG2020

Action Sequence Augmentation for Early Graph-based Anomaly Detection

Tong Zhao, Bo Ni, Wenhao Yu +3

The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behav…

cs.LG2020

A Unified View on Graph Neural Networks as Graph Signal Denoising

Yao Ma, Xiaorui Liu, Tong Zhao +3

Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a…