Search Results for author:
Found 18 papers, 12 papers with code
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings
We propose a novel fairness-aware sequential recommendation task, in which a new metric, interaction fairness, is defined to estimate how recommended items are fairly interacted by users with different protected attribute groups.
WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia
Self-contradiction is one of the low-quality article types in Wikipedia.
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data
In this work, we propose a novel research task, adversarial defenses against GNN-based privacy attacks, and present a graph perturbation-based approach, NetFense, to achieve the goal.
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks
Ignoring stock relationships will miss the information shared between stocks while using pre-defined relationships cannot depict the latent interactions or influence of stock prices between stocks.
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding
The basic idea of CoANE is to model the context attributes that each node's involved diverse patterns, and apply the convolutional mechanism to encode positional information by treating each attribute as a channel.
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning
While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage.
Ranked #2 on
Zero-shot Relation Classification
on Wiki-ZSL
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation
In this work, we aim to present the holistic SR that simultaneously accommodates conventional, inductive, and transferable settings.
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting
Forecasting spatio-temporal correlated time series of sensor values is crucial in urban applications, such as air pollution alert, biking resource management, and intelligent transportation systems.
Reinforcement Learning Enhanced Heterogeneous Graph Neural Network
Specifically, RL-HGNN models the meta-path design process as a Markov Decision Process and uses a policy network to adaptively design a meta-path for each node to learn its effective representations.
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media
In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions.
Multi-grained Semantics-aware Graph Neural Networks
Specifically, a differentiable pooling operator is proposed to adaptively generate a multi-grained structure that involves meso- and macro-level semantic information in the graph.
Hierarchical Message-Passing Graph Neural Networks
To deal with these two issues, we propose a novel Hierarchical Message-passing Graph Neural Networks framework.
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection
Intentional manipulation of invoices that lead to undervaluation of trade goods is the most common type of customs fraud to avoid ad valorem duties and taxes.
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media
This paper solves the fake news detection problem under a more realistic scenario on social media.
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm
Unsupervised embedding learning aims to extract good representation from data without the need for any manual labels, which has been a critical challenge in many supervised learning tasks.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
Human-computer conversation systems have attracted much attention in Natural Language Processing.
Towards a More Reliable Privacy-preserving Recommender System
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether.
