Search Results for author:
Found 41 papers, 24 papers with code
Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
We employ a probing technique to extract representations from different layers of the model and apply these to classification tasks.
Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs
Distribution shifts on graphs -- the data distribution discrepancies between training and testing a graph machine learning model, are often ubiquitous and unavoidable in real-world scenarios.
Multitask Active Learning for Graph Anomaly Detection
Firstly, by coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies.
Towards Self-Interpretable Graph-Level Anomaly Detection
In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i. e., the vital subgraph that leads to the predictions.
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs
Self-supervised representation learning on text-attributed graphs, which aims to create expressive and generalizable representations for various downstream tasks, has received increasing research attention lately.
UPREVE: An End-to-End Causal Discovery Benchmarking System
Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making.
Federated Few-shot Learning
In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients.
Uncertainty-Aware Robust Learning on Noisy Graphs
Graph neural networks have shown impressive capabilities in solving various graph learning tasks, particularly excelling in node classification.
Virtual Node Tuning for Few-shot Node Classification
Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task.
Learning Strong Graph Neural Networks with Weak Information
Accordingly, we propose D$^2$PT, a dual-channel GNN framework that performs long-range information propagation not only on the input graph with incomplete structure, but also on a global graph that encodes global semantic similarities.
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval.
MetaGAD: Learning to Meta Transfer for Few-shot Graph Anomaly Detection
However, the work exploring limited labeled anomalies and a large amount of unlabeled nodes in graphs to detect anomalies is rather limited.
Characterizing Long-Tail Categories on Graphs
However, there is limited literature that provides a theoretical tool to characterize the behaviors of long-tail categories on graphs and understand the generalization performance in real scenarios.
STERLING: Synergistic Representation Learning on Bipartite Graphs
Most recent bipartite graph SSL methods are based on contrastive learning which learns embeddings by discriminating positive and negative node pairs.
Few-shot Node Classification with Extremely Weak Supervision
Recent few-shot node classification methods typically learn from classes with abundant labeled nodes (i. e., meta-training classes) and then generalize to classes with limited labeled nodes (i. e., meta-test classes).
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society.
Ranked #1 on
Graph Classification
on UPFD-POL
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed.
GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods.
Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcity
The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice.
Task-Adaptive Few-shot Node Classification
Then we transfer such knowledge to the classes with limited labeled nodes via our proposed task-adaptive modules.
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
Supervised Graph Contrastive Learning for Few-shot Node Classification
Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis.
Few-Shot Learning on Graphs
In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.
Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning
In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods.
Data Augmentation for Deep Graph Learning: A Survey
In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems.
Session-based Recommendation with Hypergraph Attention Networks
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.
Graph Few-shot Class-incremental Learning
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems.
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks.
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
Sequential Recommendation for Cold-start Users with Meta Transitional Learning
A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items.
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.
FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements
The growing use of social media has led to drastic changes in our decision-making.
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
Existing efforts can be mainly categorized as spectral-based and spatial-based methods.
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Network anomaly detection aims to find network elements (e. g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority.
Fact-Enhanced Synthetic News Generation
The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy.
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Text classification is a critical research topic with broad applications in natural language processing.
Combating Disinformation in a Social Media Age
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
GLOW : Global Weighted Self-Attention Network for Web Search
Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval.
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.
Feature Interaction-aware Graph Neural Networks
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks.
Deep Anomaly Detection on Attributed Networks
In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data.
