Tag
This article presents SSAKG 2.0, an open-source software package for constructing and operating Structural Sequential Associative Knowledge Graphs (SSAKGs) for context-based sequence retrieval, with new efficient algorithms implemented in Python and C.
A developer shows that Apache DataFusion can perform billion-scale graph analytics like PageRank and weakly connected components on a laptop with 5–10GB RAM by offloading data to disk, challenging the need for Spark/GraphFrames.
Promotion of the book 'Graph Algorithms for Data Science' which teaches graph algorithms and their applications using Neo4j, covering topics like knowledge graphs, social network analysis, and node embeddings.
This paper studies small language models (SLMs) as closed-loop policies for graph algorithm execution, evaluating both step accuracy and rollout reliability across multiple graph procedures. The results show a gap between local decision quality and global execution reliability, especially for weighted algorithms.
This paper investigates whether aggregate structural invariants, specifically spectral bounds, can accelerate continuous subgraph matching (CSM) over dynamic graphs. It characterizes limitations of lazy spectral maintenance, shows exact maintenance is affordable when selective, and demonstrates pruning power of up to 51% in benchmarks.
This paper introduces GraphDC, a divide-and-conquer multi-agent framework that decomposes graph algorithmic tasks into subgraphs for specialized agents, improving scalability and reasoning performance on complex graph structures.