Fully Inductive Cardinality Estimation

InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs

Logical Multi-Hop Query Answering over Knowledge Graphs (KGs) can be formulated as querying, with an implicit completeness assumption. Current works mainly focus on Existential First Order Logic (EFO) queries. These EFO queries contain conjunction, disjunction, and negation operators. Most existing works employ transductive reasoning, meaning they are not capable of reasoning over entities unseen during training. In the real world, there is a resource scarcity, and we cannot train a model with all the nodes of a large KG. Hence, we propose InductWave, a wavelet-based inductive embedding method for logical query answering on large KGs. Here, the training graph consists of fewer nodes than the test graph. Our model performs on par with the baseline models while having half the number of message-passing layers. It outperforms all of them in most cases, with 75% of the layers. These fewer resource requirements enable us to evaluate InductWave on massive graphs, such as Wiki-KG. We test our model using extensive experiments across varying train-test graph proportions of the FB15k-(237) dataset, comparing it with the state-of-the-art models. The code and datasets for the model are available at https://github.com/kracr/inductwave/.

A Unifying Relational Perspective on Expressive Lottery Tickets

It is proved that sufficiently parameterized RGNNs contain sparse subnetworks that maintain 1-RWL expressivity and derive a lower bound on the probability that a random pruning yields such a subnetwork.

KGCache: Amortized Subgraph Retrieval for KG Reasoning with LLMs

Large language models can answer knowledge-intensive questions more reliably when they are grounded with knowledge graphs, but systems such as Think-on-Graph and Reasoning-on-Graph repeatedly query the same graph neighborhoods across different questions. In this work, we study this repeated retrieval in Knowledge Graph Question Answering~(KGQA) workloads and propose KGCache, an in-memory cache for one-hop knowledge graph neighborhoods. KGCache is designed to be compatible with both iterative traversal (ToG) and one shot planning (RoG) KGQA paradigms. KGCache is placed between the KGQA engine and the backend serving the KG, so repeated entity requests can be served from cache instead of issuing new KG queries. We evaluate KGCache on WebQSP and CWQ using LRU, LFU, and a trace-aware Oracle policy. Our analysis shows that both datasets contain substantial entity reuse among starting entities and entities reached during traversal. We also explore semantic caching for similar queries, which shows additional hit-rate gains on WebQSP and needs further accuracy testing on CWQ. Entity caching accelerates KG retrieval by up to $1.91\times$, while semantic-context caching achieves up to $1.06\times$ full-system speedup in the evaluated WebQSP configurations, with each hit being up to $3.73\times$ faster.

Edge-AdaptiveKG: Resource-Efficient Knowledge Graph Construction for RAG on Edge Devices

Integrating Knowledge Graphs (KGs) into Retrieval-Augmented Generation (RAG) can substantially improve LLM performance on complex question answering (QA) by reducing hallucinations and supplying structured context. However, building high-quality KGs over large corpora for edge scenarios is challenging: cloud-based processing introduces latency and dependency on remote services, while exhaustive on-device construction with LLMs is often computationally infeasible under limited hardware budgets. We observe that traditional non-LLM methods can efficiently capture explicit knowledge, and that real-world queries typically touch only a small, highly concentrated portion of the graph. As a result, static and exhaustive KG construction is redundant and inefficient. We propose Edge-AdaptiveKG, a resource-aware framework that combines an offline Seed KG (S-KG) with an online Query-driven KG (Q-KG). Lightweight non-LLM methods build the S-KG, while the LLM is invoked on demand during question answering to incrementally expand the Q-KG only when complex relations are needed. Experiments show that Edge-AdaptiveKG reduces computational overhead and inference latency, enabling KG-enhanced RAG on resource-constrained devices while maintaining competitive QA accuracy.

UGD-LTR: an uncertainty-aware graph-based deep learning framework for learning to rank

One-for-All Community Search on Unseen Graphs

A Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner and a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs.