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Found 10 papers, 8 papers with code
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
In our GNN-RAG framework, the GNN acts as a dense subgraph reasoner to extract useful graph information, while the LLM leverages its natural language processing ability for ultimate KGQA.
Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization
PackLLM performs model fusion by solving an optimization problem for determining each LLM's importance, so that perplexity over the input prompt is minimized.
SemPool: Simple, robust, and interpretable KG pooling for enhancing language models
Our method, termed SemPool, represents KG facts with pre-trained LMs, learns to aggregate their semantic information, and fuses it at different layers of the LM.
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient Selection
Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL).
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Different from conventional knowledge distillation, GRAD jointly optimizes a GNN teacher and a graph-free student over the graph's nodes via a shared LM.
ReaRev: Adaptive Reasoning for Question Answering over Knowledge Graphs
Our method, termed ReaRev, introduces a new way to KGQA reasoning with respect to both instruction decoding and execution.
Ranked #1 on
Semantic Parsing
on WebQuestionsSP
TempoQR: Temporal Question Reasoning over Knowledge Graphs
The first computes a textual representation of a given question, the second combines it with the entity embeddings for entities involved in the question, and the third generates question-specific time embeddings.
Ranked #1 on
Question Answering
on CronQuestions
HeMI: Multi-view Embedding in Heterogeneous Graphs
Many real-world graphs involve different types of nodes and relations between nodes, being heterogeneous by nature.
Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks
Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and network science applications including {the Internet of Things (IoT)}, finance, security, to list a few.
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
Motivated by this observation, we propose a graph representation learning method called Graph InfoClust (GIC), that seeks to additionally capture cluster-level information content.
Ranked #2 on
Link Prediction
on Citeseer
