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Found 12 papers, 2 papers with code
Deep Outdated Fact Detection in Knowledge Graphs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.
