The paper introduces ImbalancE, an inference‑time latent search method that mitigates degree imbalance bias in Knowledge Graph Embedding models. It targets the problematic prediction of target entities with much lower degrees than anchor entities, a common issue in recommender systems and other applications. Experiments on benchmark datasets show that ImbalancE improves predictions on the most imbalanced triples compared to conventional methods.
By Alberto Bernardi, Luca Costabello, Christophe Gueret
Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications. Due to their inherent incompleteness, knowledge graph completion (KGC) is widely studied and is typically formulated as triplet prediction, with link prediction as the dominant paradigm.
PEARL is a new framework for inductive knowledge graph completion that treats relational paths as context-conditioned reasoning signals. It builds a query‑specific contextual subgraph from the query entities’ neighborhoods and uses a large language model‑guided retriever to select semantically relevant paths. By constructing a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, and applying a dual‑view contrastive objective, PEARL adapts path embeddings to local and global structural evidence, achieving the best average Hits@10 on WN18RR, FB15k‑237, and NELL‑995.
By Yunchi Yang, Longlong Li, Cunquan Qu
arXiv:2606. 29860v1 Announce Type: new Abstract: Knowledge graphs (KGs) organize real-world knowledge as triplets and underpin many downstream applications.
By Zihao Zheng, Borui Cai, Yao Zhao, Keshav Sood, Yong Xiang
arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.
By Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang
arXiv:2606. 00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems.
By Feifan Jiang, Yinan Bu, Shihao Wu, Gongjun Xu, Ji Zhu
arXiv:2608.28991v1 Announce Type: cross
Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Exist...
By Haijie Xu, Chen Zhang
arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.
By Mohommad Esmaei Khani, Mahdieh Hasheminejad, Ali Taherkhani, Hossein Hajiabolhassan
The paper introduces Discrete Latent Reasoning (DLR), a method that transforms continuous latent states into explicit discrete tokens by rendering textual chains of thought into images, extracting visual features, and clustering them to build a discrete latent vocabulary. DLR enables standard autoregressive modeling over both natural language and latent tokens, supporting pretraining alignment, supervised fine-tuning, and reinforcement learning. Experiments on five reasoning benchmarks and two model series (Qwen3‑VL and LLaMA‑3) show that DLR outperforms prior latent reasoning baselines with up to 20× compression while preserving interpretable semantic structure.
By Shuochen Chang, Qingyang Liu, Shaobo Wang, Bingjie Gao, Qianli Ma, Haonan Zhao, Yibo Miao, Yulin Sun, Zelin Peng, Jiangtong Li, Li Niu
Bridge-LP is a new framework for link prediction on text‑attributed graphs that combines Graph Neural Networks and Language Models. It introduces three design components: jumping connections for robust language model training, compressed embeddings to encode neighborhood information, and a cache embedding strategy to reduce computational cost. Experiments on nine datasets show that Bridge‑LP outperforms 40 existing models, achieving up to a 43.06% improvement in Mean Reciprocal Rank.
By Chen Shao, Ruirui Wang, Tobias Kafer, Danai Koutra
arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.
By Dongxiao He, Ruqiong Zhang, Zhizhi Yu, Ling Ding, Di Jin, Guangquan Xu, Zhiyong Feng
arXiv:2610.02517v1 Announce Type: new
Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection an...
By Yu Song, Zhigang Hua, Yan Xie, Bingheng Li, Jingzhe Liu, Bo Long, Jiliang Tang, Hui Liu