The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup.
Abstract
Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full recomputation preserves the semantics of mature static solvers, but it repeatedly processes unchanged graph regions when updates are small. Solver-specific dynamic methods can reduce this cost, but their update rules often have limited transferability across objectives, feature representations, and implementations. In addition, localizing computation only by graph distance may omit community context needed by high-quality solvers. We introduce ComNetX, a solver-agnostic hierarchical adaptation framework for local dynamic updates. ComNetX maintains a multi-level community state, expands the updated region, closes it over affected communities, and contracts these communities into compact local instances. This affected-community closure and contraction preserve solver context while restricting computation to the changed part of the graph. The same interface can wrap modularity heuristics, graph-clustering models that use node features, and native dynamic solvers as local backends. We evaluate ComNetX through a multi-backend study on six real networks, longer real-data streams for topology-based backends, and controlled dynamic stochastic block model stress streams. The results show that ComNetX can preserve the quality of strong modularity-based solvers while reducing update time on large graphs: in paired runs on the largest real graph, Local Leiden keeps final modularity within 0.006 of full-snapshot recomputation while achieving a 41.9 +/- 0.2x speedup. The combined protocols also identify regimes where locality breaks down and a full refresh is preferable.
Automatic feature engineering (AutoFE) for tabular data requires discovering informative transformations from a large program space. Existing approaches suffer from three limitations: classical methods rely on fixed operator libraries with limited expressivity, LLM-based methods generate proposals from static prompts without retaining search experience, and evolutionary methods use fixed migration policies that ignore task-specific cross-family transfer utility. We introduce TOPOFE, a framework that formulates AutoFE as graph-structured multi-island evolutionary program search. The transformation space is partitioned into semantically coherent families, each explored by an island through LLM-guided mutation and crossover. Each island maintains a Prompt Adaptation Memory that accumulates accept/reject feedback to steer proposals toward productive regions without parameter updates. To coordinate global exploration, TOPOFE dynamically learns a directed topology graph whose edge weights encode transfer utility between transformation families. Cross-island transfer is triggered by adaptive saturation detection and performed through LLM-mediated hybrid synthesis, enabling discovery of compositional feature programs that cannot emerge from isolated local search. Experiments on 29 tabular datasets show that TOPOFE consistently outperforms most state-of-the-art AutoFE methods on classification and regression tasks. Beyond predictive performance, TOPOFE produces feature sets with lower redundancy and higher representational coverage, while the learned topology graph acquires meaningful task-specific transfer structure correlated with downstream gains. The discovered feature programs transfer reliably across diverse predictors and LLM backbones, demonstrating that improvements arise from TOPOFE's structured search and adaptive coordination rather than backbone-specific generation capability.
This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms originally designed for static graphs. Built on the NVIDIA RAPIDS ecosystem, the framework enables the characterization and tracking of communities in snapshot-based dynamic graphs, either by Leiden greedy optimization with multi-GPU support via Dask-based workload distribution, or eigendecomposition of a symmetric Bethe-Hessian operator. Our multislice modularity backend achieves up to roughly three orders of magnitude speedup over the CPU reference under an equal-work budget, depending on graph density and snapshot count, while preserving compatibility with existing graph analytics pipelines. We demonstrate its applicability on real-world and synthetic datasets, facilitating exploratory analysis of structural network properties over time. Such capabilities are relevant across several application domains, such as epidemic spreading, financial systems, cybersecurity, and trajectory and mobility analysis. We release our implementation as free and open-source software, including Python bindings through the NetworkX-Temporal library for ease of use and zero-code acceleration with existing codebases.
N. A. Passos, Emanuele Carlini, Salvatore Trani· 1 citation
Experiments show that AP-REASONER outperforms baseline subsamplers on structure-sensitive downstream tasks and enables controllable recovery of alternative protein conformations, highlighting the value of modeling MSA subsampling as a controllable optimization problem, where factor-graph reasoning offers an effective alternative to heuristic selection.
Zhangzhi Xiong, Minzhang Li, Hao Yu et al.· 0 citations
In extensive experiments with over 50 real-world and randomly generated graphs, it is shown that across nearly all test cases, a member of this algorithm suite matches or surpasses h-louvain and provides a more faithful community representation than the state of the art.
Fabian Brandt-Tumescheit, Henning Meyerhenke· Social Network Analysis and...· 0 citations
A Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner and a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs.
Mo Li, Zhaosong Zhao, Linlin Ding et al.· Annual International ACM SIG...· 0 citations
Dynamic graph anomaly detection (DGAD) is critical for a wide range of applications where abnormal events are rare, evolving, and tightly coupled with temporal context. Despite rapid progress in modeling dynamic graphs, the evaluation of DGAD methods remains fragmented, leaving the strengths, limitations, and trade-offs of state-of-the-art models poorly understood. We introduce DyGADBench, a comprehensive benchmark designed specifically for Dy namic G raph A nomaly D etection. It defines a diverse set of injected anomaly patterns spanning localized, global, and temporally persistent behaviors, reflecting a wide range of real-world scenarios; introduces a unifying taxonomy that organizes DGAD methods along core design axes, clarifying architectural and temporal modeling choices; and provides a unified and reproducible evaluation pipeline. We conduct an extensive empirical study of state-of-the-art DGAD models. Our findings reveal that detection difficulty increases consistently with anomaly complexity, from simple localized irregularities to coordinated and temporally persistent structures. We uncover a fundamental tension between architectural biases: methods emphasizing local structural information perform well on structure-dominated anomalies, while methods leveraging global temporal context excel on long-range anomalies, yet no approach reliably handles both. Moreover, scalability emerges as a critical bottleneck, with many high-performing methods incurring prohibitive computational or memory costs on large dynamic graphs. Together, these findings provide systematic insights into the interplay between anomaly characteristics, model design, and scalability, and point toward key directions for future research in dynamic graph anomaly detection. DyGADBench is publicly available at: https://github.com/Dastamn/dgadb.
Mohamed Nazim Mezhoudi, Guillaume Lachaud, Yanlei Diao et al.· Proceedings of the 32nd ACM...· 0 citations
