Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2502.18497

LD-Leiden: Local Parallel Community Detection in Large Dynamic Networks

Grigoriy Bokov·Aleksandr Konovalov·Anna Uporova·Stanislav Moiseev·Ivan Safonov·Alexander Radionov
View original source ↗Read source document ↗Export metadata ↓

Abstract

Dynamic community detection must update high-quality modularity partitions after edge batches, yet full Leiden reruns make small changes scale with the whole snapshot. Existing dynamic methods reduce work but often alter Leiden refinement, keep limited hierarchy state, or restrict graph support. This paper presents LD-Leiden, a local dynamic Leiden method for weighted directed and undirected graphs that preserves the move-refine-aggregate pipeline and updates only repaired affected regions. Its novelty is the combination of an affected-frontier rule after statistic repair, exact subtract-add aggregate repair, and conflict-filtered parallel local moves; together these mechanisms bound update cost by the visited frontier rather than the full graph. On real streams and streamed static graphs with up to 214M vertices and 3.30B edges, LD-Leiden is 48.77x faster than warm-started Leidenalg in 100-batch runs while preserving a 0.996 final modularity ratio. On the common undirected benchmark set, it is 6.94x faster than DF-Leiden and 9.73x faster than NetworKit while obtaining higher final modularity; synthetic sequences support the predicted local edge-volume scaling.

Explore related subjects

cs.SIcs.DCcs.DM

Keep this discovery

BibTeXRIS

Grigoriy Bokov, Aleksandr Konovalov, Anna Uporova, Stanislav Moiseev, Ivan Safonov, Alexander Radionov. 2025-02-20. LD-Leiden: Local Parallel Community Detection in Large Dynamic Networks. https://arxiv.org/abs/2502.18497

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Link prediction in complex networks via fusing node centrality and local similarity indices

Local similarity indices are widely used in link prediction on complex networks owing to their low computational cost; however, in sparse networks they assign a zero score to every node pair lacking common neighbors, which severely limits their predictive power. A natural remedy is to fuse node centrality indices with local similarity indices: the former provide global importance for the node pair, while the latter capture fine-grained local topology, and the two can be combined into complementary scores within a unified framework. This paper uses PageRank and DomiRank as two representative centrality measures and constructs a centrality--local-similarity fusion framework. The PageRank-based fusion proposed by Charikhi is first generalized to seven classical local similarity indices, and the universality of its improvement is systematically verified on nine real-world network datasets. Furthermore, the DomiRank centrality is introduced to build the DR-MD series of fused indices under a unified weighting coefficient, which overcomes the drawback that the PageRank-based fusion requires index-by-index weight tuning. Results of five-fold cross-validation together with Wilcoxon signed-rank tests show that, under the unified experimental protocol, all DR-MD indices consistently outperform the corresponding local baselines and their PR-MD counterparts on all nine datasets ($p=0.002$), and that the improvements remain robust against perturbations of $\sigma$ and the weighting coefficients within the near-critical parameter plateau; in particular, DR-RA achieves an average AUC of 0.7084, surpassing global methods such as Katz and RWR as well as several advanced similarity indices. The framework is inherently extensible, and its fusion paradigm can be straightforwardly generalized to couple other node centrality indices with local similarity indices.

cs.SI↗
arXiv2026

Chance, Persistent Advantage, and the Generative-AI Era in Open-Source Package Careers

Studies of careers in science, film, music, and books report a common pattern. When a person's most successful work arrives is close to a random draw over the works they produce. How large their successes tend to be, in contrast, follows a stable, person-specific factor. We test whether this pattern holds for open-source software careers and whether it changed when generative AI coding tools arrived. From the complete public record of GitHub push events (2015-2025), we reconstruct 102.2M career works by 6.15M contributors, and for the 908k contributors whose repositories publish packages, we measure each work's impact by how many downstream packages come to depend on it. First, we find that the timing of a career's biggest hit is close to a lottery over their works, as in science and the arts, with a small, replicable lean toward early career that grows as careers get longer. Second, some coders reliably produce higher-impact work than others, but this lasting personal factor accounts for only part of why impact persists (about a fifth in our primary specification); the rest behaves like momentum, success feeding on itself for a period of time. Third, within the same contributors, this structure did not change after ChatGPT's release. The stable factor's weight grew by about as much as it grew for an earlier cohort that simply aged, and subtracting the effect of aging from the effect of generative AI puts the shift at +0.03 (95% CI [-0.22, +0.23]), indistinguishable from zero. The success pattern documented in science and the arts therefore describes open-source careers too, and it shows no detectable break across the arrival of generative AI. These results have implications for how track records on open platforms should be read and on what to expect from generative AI for the careers built on them.

cs.SI↗
arXiv2026

How neighbourhood ideology shapes misinformation belief in densely tied social networks

With the rapid spread of news on social media, understanding the propagation of misinformation is becoming increasingly important. One factor that affects individuals' vulnerability to false information is their ideological predisposition. Despite the large number of agent-based models that focus on social influence as a driver of the spread of false claims, they often fail to explicitly integrate personal ideological biases into belief formation. In this work, we explore how misinformation spreads through the interaction between individuals' ideological biases and social influence. Our model accounts for both the strength of individuals' ideological biases and the extent to which a false claim aligns with their ideology. Social influence modifies the effects of ideological intensity and false claim alignment through network interactions. Notably, the influence of neighbours' ideological intensity on belief is strongly affected by how well those neighbours are connected to one another. These results highlight the importance of considering both network structure and personal ideological biases when modelling misinformation propagation.

cs.SI↗