arXiv subjects

Guangzhi Qu

Publications and source records attributed to Guangzhi Qu.

5 recordsLinked to original sources

arXiv2026

When Does Explicit View Routing Work? A Controlled Study of Multi-View Graph-Text Alignment

Graph-text retrieval typically maps a graph and its description to a single embedding, even when a query concerns only one semantic aspect, such as a class label or molecular property. Multiple heads can separate these aspects, but a change in the query head may alter retrieval even when the wrong text is sent to that head. Such behavior demonstrates architectural channelization, not necessarily semantic routing. We examine the conditions under which this distinction can be resolved. Our controlled version of MV-GTA uses deterministic, verifiable text segments; isolated text encoders; view-specific graph heads; and relevance derived from external labels or RDKit descriptors. Correct routing and per-sample derangements form a causal test of whether retrieval depends on content. On BBBP and BACE, correct routing improves label and property nDCG by 0.305 to 0.685 over deranged training. The expected graph head exceeds the best wrong head by 0.303 to 0.453. Topology does not specialize consistently across the two datasets. In a matched three-seed comparison, one joint model obtains mean topology, label, and property nDCG of 0.720/1.000/0.877; three separately trained Single specialists obtain 0.633/0.976/0.859. Property paraphrase augmentation also improves unseen-template nDCG by 0.140 and 0.147 over a matched-exposure canonical control. Consistency and hard-template extensions, however, reduce canonical retrieval in some settings. The evidence is therefore limited to explicit, externally grounded label and property routing and observed multi-interface consolidation. It does not establish free-form routing, consistent three-view specialization, statistical equivalence to specialists, or superior downstream prediction.

cs.LG↗

arXiv2026

LiteSemRAG: Lightweight LLM-Free Semantic-Aware Graph Retrieval for Robust RAG

Graph-based Retrieval-Augmented Generation (RAG) has shown great potential for improving multi-level reasoning and structured evidence aggregation. However, existing graph-based RAG frameworks heavily rely on exploiting large language models (LLMs) during indexing and querying, leading to high token consumption, computational cost and latency overhead. In this paper, we propose LiteSemRAG, a lightweight, fully LLM-free, semantic-aware graph retrieval framework. LiteSemRAG constructs a heterogeneous semantic graph by exploiting contextual token-level embeddings, explicitly separating surface lexical representations from context-dependent semantic meanings. To robustly model polysemy, we introduce a dynamic semantic node construction mechanism with chunk-level context aggregation and adaptive anomaly handling. At query stage, LiteSemRAG performs a two-step semantic-aware retrieval process that integrates co-occurrence graph weighting with an isolated semantic recovery mechanism, enabling balanced structural reasoning and semantic coverage. We evaluate LiteSemRAG on three benchmark datasets and experimental results show that LiteSemRAG achieves the best mean reciprocal rank (MRR@10) across all datasets and competitive or superior recall rate (Recall@10) compared to state-of-the-art LLM-based graph RAG systems. Meanwhile, LiteSemRAG consumes zero LLM tokens and achieves substantial efficiency improvements in both indexing and querying due to the elimination of LLM usage. These results demonstrate the effectiveness of LiteSemRAG, indicating that a strong semantic-aware graph retrieval framework can be achieved without relying on LLM-based approaches.

cs.IR↗

arXiv2025

Edged Weisfeiler-Lehman Algorithm

As a classical approach on graph learning, the propagation-aggregation methodology is widely exploited by many of Graph Neural Networks (GNNs), wherein the representation of a node is updated by aggregating representations from itself and neighbor nodes recursively. Similar to the propagation-aggregation methodology, the Weisfeiler-Lehman (1-WL) algorithm tests isomorphism through color refinement according to color representations of a node and its neighbor nodes. However, 1-WL does not leverage any edge features (labels), presenting a potential improvement on exploiting edge features in some fields. To address this limitation, we proposed a novel Edged-WL algorithm (E-WL) which extends the original 1-WL algorithm to incorporate edge features. Building upon the E-WL algorithm, we also introduce an Edged Graph Isomorphism Network (EGIN) model for further exploiting edge features, which addresses one key drawback in many GNNs that do not utilize any edge features of graph data. We evaluated the performance of proposed models using 12 edge-featured benchmark graph datasets and compared them with some state-of-the-art baseline models. Experimental results indicate that our proposed EGIN models, in general, demonstrate superior performance in graph learning on graph classification tasks.

cs.LG↗

arXiv2025

GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks

One significant challenge of exploiting Graph neural networks (GNNs) in real-life scenarios is that they are always treated as black boxes, therefore leading to the requirement of interpretability. To address this, model-level interpretation methods have been developed to explain what patterns maximize probability of predicting to a certain class. However, existing model-level interpretation methods pose several limitations such as generating invalid explanation graphs and lacking reliability. In this paper, we propose a new Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks (GIN-Graph), to generate reliable and high-quality model-level explanation graphs. The implicit and likelihood-free generative adversarial networks are exploited to construct the explanation graphs which are similar to original graphs, meanwhile maximizing the prediction probability for a certain class by adopting a novel objective function for generator with dynamic loss weight scheme. Experimental results indicate that GIN-Graph can be applied to interpret GNNs trained on a variety of graph datasets and generate high-quality explanation graphs with high stability and reliability.

cs.LG↗

arXiv2013

North Atlantic Right Whale Contact Call Detection

The North Atlantic right whale (Eubalaena glacialis) is an endangered species. These whales continuously suffer from deadly vessel impacts alongside the eastern coast of North America. There have been countless efforts to save the remaining 350 - 400 of them. One of the most prominent works is done by Marinexplore and Cornell University. A system of hydrophones linked to satellite connected-buoys has been deployed in the whales habitat. These hydrophones record and transmit live sounds to a base station. These recording might contain the right whale contact call as well as many other noises. The noise rate increases rapidly in vessel-busy areas such as by the Boston harbor. This paper presents and studies the problem of detecting the North Atlantic right whale contact call with the presence of noise and other marine life sounds. A novel algorithm was developed to preprocess the sound waves before a tree based hierarchical classifier is used to classify the data and provide a score. The developed model was trained with 30,000 data points made available through the Cornell University Whale Detection Challenge program. Results showed that the developed algorithm had close to 85% success rate in detecting the presence of the North Atlantic right whale.

cs.LG↗