Aug 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 35 references
TL;DR
The Hierarchical Heterogeneous Information Network is proposed as a data model that organizes typed entity relations in a main structure layer and descriptive evidence in a strong attribute layer as well as developing controllable approximate reduction as one instantiation.
Abstract
Real-world networks contain multiple entity and relation types, semantic paths, attribute nodes, and node features. Heterogeneous information networks (HINs) encode this heterogeneity, but placing all evidence in one graph space can obscure the distinct roles of relational structure and attributes and complicate controlled merge decisions. We propose the Hierarchical Heterogeneous Information Network (HHIN) as a data model that organizes typed entity relations in a main structure layer and descriptive evidence in a strong attribute layer. We develop controllable approximate reduction as one instantiation. Fixed point refinement grounded in behavioral equivalence yields stable structural candidates; two attribute Guards based on cosine similarity constrain merge admissibility; and a normalized semantic budget selects thresholds using similarity errors and normalized discounted cumulative gain at rank 10 (nDCG@10) computed from PathSim and HeteSim. Theory establishes termination, containment of accepted merges within stable candidate classes, and budget feasibility. On ACM, DBLP, and IMDB, the method reduces 7.22–21.78% of all nodes while retaining nDCG@10 values of at least 0.9735 for PathSim and 0.9910 for HeteSim. Across datasets, repeated retrieval and ranking yield speedups of 1.04–1.15×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document} and 1.13–1.26×\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times $$\end{document}, respectively. On DBLP, paired tests with a relational graph convolutional network (R-GCN) and a heterogeneous graph attention network (HAN) do not detect significant classification differences between the original and reduced carriers, while mean epoch costs decrease by factors of 1.10 and 1.07. DBLP KMeans clustering and transductive label prediction from similarity neighborhoods provide additional downstream evaluations.
BoxDPpS performs box-level search with safe region pruning, eliminates redundant representations of the same iRM-set, improves early pruning through bounded warm-up, and compresses each fixed-M auxiliary network for exact parametric pseudoflow solving.
Jiadong Xie, Jiaming Yang, Kangfei Zhao et al.· 0 citations
Temporal heterogeneous graphs offer a natural abstraction for dynamic relational systems in which diverse node and relation types co-exist and evolve over time. Learning on such graphs requires jointly modeling cross-type structural heterogeneity and the temporal dynamics of interactions, yet existing methods still struggle to reconcile parameter-efficient cross-type transfer with relation-aware specialization, and typically inject time only as additive features outside the attention kernel. We propose \textbf{THGFM}, a web-scale temporal heterogeneous graph fusion model that addresses both limitations within a unified dual-path architecture. THGFM couples a \textit{Shared-Space Temporal Attention} branch for parameter-efficient cross-type transfer with a \textit{Relational Type-Partitioned Temporal Attention} branch for relation-aware specialization, and integrates them through \textit{Dual-Path Relational--Shared Fusion}, instantiated with \textit{Type-Conditioned Non-Competitive Gated Sum Fusion}: a adaptive mechanism that assigns independent, type-conditioned feature-wise gates to the shared and specialized branches, allowing both to be amplified or suppressed without zero-sum competition. To directly incorporate relative time into the attention score, THGFM further introduces \textit{Rotary Temporal Attention}, which rotates queries and keys by half-phases of relative time before matching. THGFM consistently outperforms baseline graph transformer models on academic graphs benchmarks, delivering a $+3.25\%$ six-task mean gain, with peak relative gains of $+12.37\%$ on OAG-CS PV, $+4.87\%$ on PF-$L_2$, and $+1.18\%$ on PF-$L_1$, and $+4.24\%$, $+3.73\%$, and $+4.61\%$ on OGBN-MAG, HTAG-ArXiv, and HTAG-DBLP, respectively.
Yixin Peng, Diego Collarana, Er Jin et al.· arXiv.org· 0 citations
HyMAGE models each node as an autonomous agent and leverages LLMs for local-level semantic selection, so that hyperedge formation and dissolution emerge from local semantic affinity and structural context in a self-organizing manner.
B. Gu, Ji Zeng, Nuoran Zhou et al.· Proceedings of the 32nd ACM...· 0 citations
Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query. We identify this mismatch as the representation--inference gap. We propose Agentic Context Engineering for Hierarchical GraphRAG (ACE-GraphRAG), an inference-time context policy layer that supplements and adapts the initial context for generation. ACE-GraphRAG formulates context construction as a policy over gap-aware refinement, retrieval branches, and task-conditioned adaptation. Parallel Differential Retrieval acquires supplementary evidence from depth-oriented factual and breadth-oriented semantic branches. These evidence increments are consolidated with the initial context while preserving provenance and abstraction levels. Full-ACE applies the full policy uniformly within each task family, whereas Adaptive-ACE selects task- and topology-specific policies for individual queries. We evaluate ACE-GraphRAG on HotpotQA, 2WikiMultiHopQA, and four UltraDomain subsets across multi-hop QA and query-focused summarization. Full-ACE outperforms the evaluated RAG and GraphRAG baselines across both task families, while Adaptive-ACE further improves multi-hop QA and is preferred over Full-ACE on all four UltraDomain subsets. Ablation and topology analyses support treating context construction as a query- and task-dependent inference policy rather than a fixed procedure.
Yongfeng Huang, Yuren Lai, Ruiying Chen et al.· 0 citations
Knowledge graphs have become a fundamental representation for structured knowledge, yet their incompleteness remains a major obstacle to reliable reasoning. Missing links may interrupt relational paths and limit the evidence available for downstream inference. Knowledge graph completion addresses this problem by predicting missing facts from observed relational patterns. Existing methods have made notable progress, but many of them rely on a single geometric space or fuse multiple geometric representations in a static manner. This limits their ability to capture heterogeneous graph structures, especially when hierarchical dependencies, local relational patterns, and cyclic motifs coexist in the same knowledge graph. In this paper, we propose multi-curvature progressive fusion (MCPF), a geometry-aware graph neural reasoning framework for knowledge graph completion. MCPF learns relation-aware representations in Euclidean, hyperbolic, and hyperspherical spaces, which provide complementary inductive biases for conventional relational interactions, hierarchical organization, and cyclic structures. Instead of directly aggregating these representations, MCPF progressively refines them through cross-space interactions and uses a query-conditioned gating mechanism to determine their relative contributions. This design enables the model to emphasize reliable geometric signals while suppressing less informative ones for each query. The fused representation is then used within a graph neural reasoning architecture for message passing and link prediction. Experiments on four benchmark datasets demonstrate that MCPF achieves competitive results against representative baselines and consistently outperforms its ablated variants.
This paper proposes a deterministic, explainable framework for validating uncertain entities during Knowledge Graph (KG) expansion. The contribution is conceptual and formal: we present the framework and demonstrate its internal mathematical coherence, while empirical evaluation is explicitly deferred to future work. Since adding unverified nodes is trivial but removing structurally corrupted data afterwards is computationally hard, the multiphase Let's Build Knowledge (LBK) Framework acts as a gatekeeper that prevents such corruption at the point of entry. Drawing on cognitive heuristics, molecular data patterns, and meta-reasoning, LBK offers a deterministic alternative to prevalent stochastic and embedding-based approaches in Explainable AI (XAI): it integrates Formal Concept Analysis (FCA) with a localized adaptation of Byzantine Fault Tolerance (BFT) to scrutinize incoming structures, enabling loss-free, rule-based verification of new entities before global integration. We provide a precise specification of this hybrid model and demonstrate its formal capacity for deterministic uncertainty management, without claiming empirical validation.
Simon Von Oppenkowski, Benedikt Lerch, Klemens Schnattinger· European Conference on Knowl...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
