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pro vyhledávání: '"Lee, Yeon Chang"'
The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary depe
Externí odkaz:
http://arxiv.org/abs/2408.15620
Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare. However, they often suffer from fairness issues due to inherent biases in node attributes and graph st
Externí odkaz:
http://arxiv.org/abs/2408.12875
We developed DyGETViz, a novel framework for effectively visualizing dynamic graphs (DGs) that are ubiquitous across diverse real-world systems. This framework leverages recent advancements in discrete-time dynamic graph (DTDG) models to adeptly hand
Externí odkaz:
http://arxiv.org/abs/2406.17963
Collaborative filtering (CF) methods for recommendation systems have been extensively researched, ranging from matrix factorization and autoencoder-based to graph filtering-based methods. Recently, lightweight methods that require almost no training
Externí odkaz:
http://arxiv.org/abs/2405.04746
The Fair Graph Anomaly Detection (FairGAD) problem aims to accurately detect anomalous nodes in an input graph while avoiding biased predictions against individuals from sensitive subgroups. However, the current literature does not comprehensively di
Externí odkaz:
http://arxiv.org/abs/2402.15988
Online misinformation poses a global risk with significant real-world consequences. To combat misinformation, current research relies on professionals like journalists and fact-checkers for annotating and debunking misinformation, and develops automa
Externí odkaz:
http://arxiv.org/abs/2310.02095
The problem of representing nodes in a signed network as low-dimensional vectors, known as signed network embedding (SNE), has garnered considerable attention in recent years. While several SNE methods based on graph convolutional networks (GCN) have
Externí odkaz:
http://arxiv.org/abs/2309.00816
Autor:
Jin, Yiqiao, Lee, Yeon-Chang, Sharma, Kartik, Ye, Meng, Sikka, Karan, Divakaran, Ajay, Kumar, Srijan
The problem of community-level information pathway prediction (CLIPP) aims at predicting the transmission trajectory of content across online communities. A successful solution to CLIPP holds significance as it facilitates the distribution of valuabl
Externí odkaz:
http://arxiv.org/abs/2306.02259
Autor:
Sharma, Kartik, Lee, Yeon-Chang, Nambi, Sivagami, Salian, Aditya, Shah, Shlok, Kim, Sang-Wook, Kumar, Srijan
Publikováno v:
ACM Comput. Surv. (April 2024)
Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly eff
Externí odkaz:
http://arxiv.org/abs/2212.04481
Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution
Externí odkaz:
http://arxiv.org/abs/2207.03408