136 citations · 553 across the 42 of their papers we have counts for
11 papers · 1 filter
Flashlight: Scalable Link Prediction with Effective Decoders
Yiwei Wang, Bryan Hooi, Yozen Liu +3
Link prediction (LP) has been recognized as an important task in graph learning with its broad practical applications. A typical application of LP is to retrieve the top scoring ne…
Attributed Graph Modeling with Vertex Replacement Grammars
Satyaki Sikdar, Neil Shah, Tim Weninger
Recent work at the intersection of formal language theory and graph theory has explored graph grammars for graph modeling. However, existing models and formalisms can only operate…
Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs
Hyeonjeong Shin, Taehyung Kwon, Neil Shah +1
A variety of tasks on dynamic graphs, including anomaly detection, community detection, compression, and graph understanding, have been formulated as problems of identifying consti…
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
Xianfeng Tang, Yozen Liu, Neil Shah +3
With the rapid growth and prevalence of social network applications (Apps) in recent years, understanding user engagement has become increasingly important, to provide useful insig…
Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition
Sara Abdali, Neil Shah, Evangelos E. Papalexakis
Distinguishing between misinformation and real information is one of the most challenging problems in today's interconnected world. The vast majority of the state-of-the-art in det…
SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs
Hamed Nilforoshan, Neil Shah
Given the reach of web platforms, bad actors have considerable incentives to manipulate and defraud users at the expense of platform integrity. This has spurred research in numerou…
False Information on Web and Social Media: A Survey
Srijan Kumar, Neil Shah
False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proli…
LookOut on Time-Evolving Graphs: Succinctly Explaining Anomalies from Any Detector
Nikhil Gupta, Dhivya Eswaran, Neil Shah +2
Why is a given node in a time-evolving graph (-graph) marked as an anomaly by an off-the-shelf detection algorithm? Is it because of the number of its outgoing or incoming edges…
The Many Faces of Link Fraud
Neil Shah, Hemank Lamba, Alex Beutel +1
Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior. But i…
FLOCK: Combating Astroturfing on Livestreaming Platforms
Neil Shah
Livestreaming platforms have become increasingly popular in recent years as a means of sharing and advertising creative content. Popular content streamers who attract large viewers…
EdgeCentric: Anomaly Detection in Edge-Attributed Networks
Neil Shah, Alex Beutel, Bryan Hooi +5
Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce net…