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Found 15 papers, 10 papers with code
CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship
The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position.
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks
Graph Neural Networks (GNNs) have become essential tools for graph representation learning in various domains, such as social media and healthcare.
HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
HearHere facilitates the key processes of news information consumption through two visualizations.
Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation
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.
VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation
While there exist a number of recommender systems designed for this problem, we point out that they are challenged in accurately capturing the relation (spec., the degree of relevance) between the current and each of the past visits for the patient when obtaining her current health status, which is the basis for recommending medications.
MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
In this paper, we focus on multimedia recommender systems using graph convolutional networks (GCNs) where the multimodal features as well as user-item interactions are employed together.
CROWN: A Novel Approach to Comprehending Users' Preferences for Accurate Personalized News Recommendation
Personalized news recommendation aims to assist users in finding news articles that align with their interests, which plays a pivotal role in mitigating users' information overload problem.
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (CASH) that employs (1) context-aware node aggregation to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2).
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings.
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance.
A Survey of Graph Neural Networks for Social Recommender Systems
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.
Phishing URL Detection: A Network-based Approach Robust to Evasion
Many cyberattacks start with disseminating phishing URLs.
Linear, or Non-Linear, That is the Question!
To our knowledge, we are the first who design a hybrid method and report the correlation between the graph centrality and the linearity/non-linearity of nodes.
Creating A Neural Pedagogical Agent by Jointly Learning to Review and Assess
Our model outperforms existing approaches over several metrics in predicting user response correctness, notably out-performing other methods on new users without large question-response histories.
Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model
As collaborative filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clustering-based CF (CBCF) method using an incentivized/penalized user (IPU) model only with ratings given by users, which is thus easy to implement.
