{"total":11,"items":[{"citing_arxiv_id":"2606.30270","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Cyclic Attractor Detection in Boolean Network Dynamics under Local Logical Constraints","primary_cat":"cs.CC","submitted_at":"2026-06-29T13:17:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"For every fixed k ≥ 2 the cyclic attractor detection problem is NP-complete precisely when the local Boolean function class contains majority-like self-dual rules or mixed conjunctive-disjunctive monotone families, and polynomial-time solvable in all other Post classes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23874","ref_index":43,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Identifying structural design principles shaping the computational abilities of recurrent neural networks","primary_cat":"q-bio.NC","submitted_at":"2026-06-22T19:17:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09083","ref_index":28,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Characterizing and modeling the patterns of vehicle movement on road networks","primary_cat":"physics.soc-ph","submitted_at":"2026-06-08T06:29:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Vehicle trips exhibit three phases explained by time-minimizing movement on hierarchical road networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01357","ref_index":13,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hypergraphs from multivariate connectivity: caCOH-based EEG/MEG representation","primary_cat":"q-bio.QM","submitted_at":"2026-05-31T17:29:31+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31500","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","primary_cat":"cs.LG","submitted_at":"2026-05-29T16:22:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"IO-aware GPU kernels for SpMM convolutions, degree-aware reductions, and fused attention layers deliver median speedups of 1.6-2.6x (up to 10x) and memory reductions up to 76x over DGL/PyG baselines on realistic graphs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15173","ref_index":51,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hybrid Sketching Methods for Dynamic Connectivity on Sparse Graphs","primary_cat":"cs.DS","submitted_at":"2026-05-14T17:57:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Hybrid sketching saves up to 97% space on dense graphs and 15% on sparse ones by sketching dense cores and storing sparse parts exactly, with new BalloonSketch reducing sketch sizes up to 8x.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09747","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The Matching Function: A Unified Look into the Black Box","primary_cat":"econ.TH","submitted_at":"2026-05-10T20:50:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Network structures of applicant-vacancy links determine matching function forms, with dispersion in search intensities reducing match efficacy and potentially making higher average search counterproductive.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04921","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Convolution Process for Sea Surface Temperature Hot-Spot Identification in the Mediterranean Sea","primary_cat":"stat.ME","submitted_at":"2026-05-06T13:49:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A convolution process on a directed network provides a covariance model for SST that respects physical barriers and currents, used to identify thermal hot spots via Monte Carlo RCP projections.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13963","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A generative model for bipartite gene-sharing networks","primary_cat":"q-bio.PE","submitted_at":"2026-04-15T15:14:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A mechanistic model with horizontal gene transfer, new gene capture, genome emergence, and gene loss generates scale-free gene degrees and exponential genome degrees in bipartite networks, closely matching viral and pangenome observations when gene loss rate is set to zero.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12721","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models","primary_cat":"cs.CL","submitted_at":"2026-04-14T13:36:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LLMs generate 5P causal graphs from 46 psychotherapy intake transcripts that match human expert graphs in structure and meaning, with moderate clinical usefulness ratings.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"are present), as well as centrality measures (degree [22], betweenness [23], closeness [22] for each node). We averaged these across nodes to compare overall connectedness. We also calculated clustering coefficients (local and global) and counted simple motifs (triangles) [25, 26, 27]. To see how factors were grouped, we ran three community detection algorithms - Leiden [28], Girvan-Newman [29], and Infomap [30], on each graph. We analyzed whether communities contained nodes of the same 5P category or related categories. Finally, we computed"},{"citing_arxiv_id":"2604.08793","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hierarchical Community Detection in Bipartite Networks","primary_cat":"cs.SI","submitted_at":"2026-04-09T22:08:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A new modularity function Qbg allows detection of hierarchical communities in bipartite networks at multiple scales by exploiting resolution limits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}