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Found 26 papers, 7 papers with code
Large Language Models for Bioinformatics
With the rapid advancements in large language model (LLM) technology and the emergence of bioinformatics-specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications.
Long-range Brain Graph Transformer
Furthermore, by employing the transformer framework, ALERT adaptively integrates both short- and long-range dependencies between brain ROIs, enabling an integrated understanding of multi-level communication across the entire brain.
Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
In this paper, we present a comprehensive review of methodologies for applying LLMs to graphs, termed LLM4graph.
Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach for mitigating the hallucination of large language models (LLMs) through the integration of external knowledge.
Graph Transformers: A Survey
Graph transformers are a recent advancement in machine learning, offering a new class of neural network models for graph-structured data.
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients
Compared to the solutions based on centralized data centers, updating large models in the Internet of Things (IoT) faces challenges in coordinating knowledge from distributed clients by using their private and heterogeneous data.
Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network
Ablation studies and case studies further validate the effectiveness of our HDBN.
Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems
We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates.
Higher-order Structure Based Anomaly Detection on Attributed Networks
Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks.
PANDORA: Deep graph learning based COVID-19 infection risk level forecasting
To address the current limitations, we propose a deep graph learning model, called PANDORA, to predict the infection risks of COVID-19, by considering all essential factors and integrating them into a geographical network.
Collaborative Team Recognition: A Core Plus Extension Structure
The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly.
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra.
FairGT: A Fairness-aware Graph Transformer
The design of Graph Transformers (GTs) generally neglects considerations for fairness, resulting in biased outcomes against certain sensitive subgroups.
Deep Outdated Fact Detection in Knowledge Graphs
Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains.
How Can Large Language Models Understand Spatial-Temporal Data?
We tackle the data mismatch by proposing: 1) STG-Tokenizer: This spatial-temporal graph tokenizer transforms intricate graph data into concise tokens capturing both spatial and temporal relationships; 2) STG-Adapter: This minimalistic adapter, consisting of linear encoding and decoding layers, bridges the gap between tokenized data and LLM comprehension.
Coupled Attention Networks for Multivariate Time Series Anomaly Detection
To address this issue, we propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data featuring dynamic variable relationships.
Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning
In practice, a set of formulaic alphas is often used together for better modeling precision, so we need to find synergistic formulaic alpha sets that work well together.
Quantum Graph Learning: Frontiers and Outlook
However, facilitating quantum theory to enhance graph learning is in its infancy.
CHIEF: Clustering with Higher-order Motifs in Big Networks
In the first procedure, we propose to lower the network scale by optimizing the network structure with maximal k-edge-connected subgraphs.
Graph Augmentation Learning
To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks.
Graph Learning: A Survey
In this survey, we present a comprehensive overview on the state-of-the-art of graph learning.
OFFER: A Motif Dimensional Framework for Network Representation Learning
In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined.
Multivariate Relations Aggregation Learning in Social Networks
Therefore, in graph learning tasks of social networks, the identification and utilization of multivariate relationship information are more important.
Big Networks: A Survey
This paper proposes a guideline framework that gives an insight into the major topics in the area of network science from the viewpoint of a big network.
