Tag
Darij Grinberg's 454-page graduate-level textbook 'Introduction to Graph Theory' (Spring 2025 edition, Drexel University Math 530) covers simple graphs, trees, Eulerian/Hamiltonian cycles, colorings, matchings, and network flows, with around a hundred exercises.
This paper empirically measures how language model agents adopt beliefs from peers, finding a sigmoid adoption kernel characteristic of complex contagion, where the threshold is driven by a single 'coherence' dimension combining claim plausibility, source reliability, and agent disposition. It further shows clustered-network propagation, bifurcating cascade windows, and hysteretic consensus in multi-agent populations, meaning established beliefs are far harder to dislodge than to install.
The paper introduces Fundamental Dynamical Units (FDUs) as composable primitives for physics-informed structural inference in networked dynamical systems, using neural ODEs to recover interaction structures from perturbation time-series data.
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 investigates whether dependency parsing of non-human primate vocalizations or gestures can be evaluated without a gold standard. Using network science, the authors show that the proportion of correct edges retrieved by a parser is necessarily high due to the fast decay of sequence length distributions in non-human primates, making evaluation feasible, unlike for human language.
University of Kansas researchers used network science to map visual similarities in spoken English words, finding that about one-third of words look like at least one other word when lip-read. The study offers insights into common lip-reading errors and could improve training for humans and AI lip-reading systems.