activity
20172026
most citedSelf-supervised Learning on Graphs: Deep Insights and New Direction

111 citations · 543 across the 63 of their papers we have counts for

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10 papers · 1 filter

cs.SI2026

Adverse Online Social Interactions: A Multi-Level Evolutionary Analysis of Local Patterns, Diffusion, and Community Disruption

Xueqi Cheng, Qinwen Ge, Hamid Karimi +2

Adverse social interactions (ASIs) can shape how online communities evolve over the time. However, structural-based ASIs and content-based ASIs are often studied separately and at…

cs.SI2025

Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs

Yuying Zhao, Yu Wang, Xueqi Cheng +5

The remarkable advancements in Large Language Models (LLMs) have revolutionized the content generation process in social media, offering significant convenience in writing tasks. H…

cs.SI2024★ 1 cited

BTS: A Comprehensive Benchmark for Tie Strength Prediction

Xueqi Cheng, Catherine Yang, Yuying Zhao +3

The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hinder…

cs.SI2023

Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation

Anwar Said, Mudassir Shabbir, Tyler Derr +2

Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the…

cs.SI2020

Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries

Aaron Brookhouse, Tyler Derr, Hamid Karimi +2

Information is crucial to the function of a democratic society where well-informed citizens can make rational political decisions. While in the past political entities were primari…

cs.SI2019

Balance in Signed Bipartite Networks

Tyler Derr, Cassidy Johnson, Yi Chang +1

A large portion of today's big data can be represented as networks. However, not all networks are the same, and in fact, for many that have additional complexities to their structu…