Background/Objectives: Literature-derived knowledge graphs are attractive for drug repurposing because every edge retains its supporting publications. Their predicates, however, record which relation a sentence asserted, not whether the evidence supports a therapeutic direction. We asked whether reading those publications can align such a graph with the repurposing task. Methods: EviAlign re-interprets each edge of disease-centered SemMedDB subgraphs. A large language model (LLM) judge reads the supporting publications sentence by sentence, assigns a therapeutic direction or withdraws the edge, and weights each edge by evidence strength rather than publication count. Signed edges compose along drug–mediator–disease paths. Six diseases and 68,689 edges were evaluated against the original predicates read through a fixed mapping and trimmed to the same size, using curated drug–disease benchmarks, an internal sign-consistency test, and blinded rating by three domain experts. Results: EviAlign increased average precision by 63% over the size-matched fixed-mapping baseline. For candidates with no direct disease literature, Precision@10 increased approximately threefold (0.050 versus 0.018). Direct and mediator-derived signs agreed in 75.0% of evaluable cases, 16 percentage points above a shuffled-label control; the fixed mapping stayed at the level of its own control. Experts confirmed the re-interpreted direction in 34 of 36 reversal ratings and judged 80% of top-ranked mediator-only candidates worth investigating, versus 53% of low-ranked controls. Conclusions: Reading the evidence behind each edge aligns literature-derived knowledge graphs with drug repurposing and yields interpretable, mechanism-grounded hypotheses for further investigation.

Background: Mechanistic reporting from large-scale biomedical knowledge graphs remains challenging, particularly when integrating structured graph evidence with large language model (LLM)–based explanation in a reproducible and auditable manner. Existing approaches either rely on manual synthesis of graph-derived results or generate unconstrained narratives that lack traceability to underlying evidence. Methods: We present SemNet Explorer, an evidence-grounded knowledge graph–LLM unified framework for automated mechanistic reporting across biomedical domains using SemNet 2.0, a PubMed-scale heterogeneous knowledge graph. Given a set of target concepts and a selected semantic layer, the framework organizes graph-derived evidence into structured regions and generates two complementary report types: global reports for process-level mechanisms and anchor-centric reports for localized mediator-based explanations. A central methodological contribution is an ablation-derived adaptive grounding policy: we systematically compare alternative evidence-integration strategies across report types, semantic layers, and region structures, and use the resulting preferences to guide prompt selection in the deployed system. Results: SemNet Explorer produces stable region decompositions and interpretable report scaffolds across molecular (AAPP), disease-level (DSYN), and pharmacologic (PHSU) representations. For global reports, explicit evidence grounding improves expression quality more consistently than content accuracy, with benefits dependent on evidence density and semantic abstraction. In contrast, anchor-centric reports show consistent improvements in both content and expression under stronger, mediator-constrained prompting. These findings are supported by both pairwise ablation comparisons and absolute score analyses. Conclusions: SemNet Explorer establishes a generalizable unified framework and interactive platform for transforming knowledge graph evidence into reproducible mechanistic narratives across biomedical domains, including multimorbidity analysis, comparative pathophysiology, drug repurposing, and adverse event discovery. The results demonstrate that effective knowledge graph–LLM integration requires adaptive, context-dependent evidence grounding rather than fixed prompting strategies.

Temporal network diffusion models play a crucial role in healthcare, information technology, and machine learning, enabling the analysis of dynamic event-based processes such as disease spread, information propagation, and behavioral diffusion. This study addresses the challenge of reconstructing temporal network diffusion events in real time under conditions of missing and evolving data. A novel non-parametric reconstruction method by simple weights differentiationis proposed to enhance source detection robustness with provable improved error bounds. The approach introduces adaptive cost adjustments, dynamically reducing high-risk source penalties and enabling bounded detours to mitigate errors introduced by missing edges. Theoretical analysis establishes enhanced upper bounds on false positives caused by detouring, while a stepwise evaluation of dynamic costs minimizes redundant solutions, resulting in robust Steiner tree reconstructions. Empirical validation on three real-world datasets demonstrates a 5% improvement in Matthews correlation coefficient (MCC), a twofold reduction in redundant sources, and a 50% decrease in source variance. These results confirm the effectiveness of the proposed method in accurately reconstructing temporal network diffusion while improving stability and reliability in both offline and online settings.