AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work studies a new query-efficient cycle basis (QCB) problem and devise efficient algorithms to find QCB with enhanced efficiency to enumerate cycles, and demonstrates the efficiency and scalability of the cycle enumeration algorithm based on QCB.
This work introduces a novel edge-centric framework that treats temporal edges as the core units of exploration and eliminates redundant temporal checks, and extends this framework to dynamic settings by introducing an efficient incremental update algorithm that selectively identifies affected paths only.
This survey will help researchers better understand and gain useful insights into the large and complex design space of out-of-core graph processing, including graph preprocessing, graph algorithm execution, utilization of emerging storage devices, and miscellaneous optimizations.
Experimental results demonstrate that TWJtr-D significantly reduces computation time and outperforms state-of-the-art methods which process in a similar pattern, highlighting the efficiency of TWJtr-D for triangle detection in large-scale networks.
Centroids are used to compute an arbitrary number of simple paths with some important benefits: the expansion of a single centroid delivers an arbitrary number of paths; only a single Dijk-stra search is required to complete the task; the same algorithm can be easily coupled with heuristics that improve search efficiency.
The E2 Algorithm for constructing the Hamiltonian cycle in an arbitrary graph without edges’ weights is proposed and whether some general graphs are Hamiltonian is shown, to prove the correctness of the method.
Cohesive subgraph mining is a fundamental task in graph data analytics. We re-visit the problem of listing all minimal $k$-cores, where a $k$-core is a subgraph in which every vertex has degree at least $k$, and minimality requires that no proper subset remains a $k$-core. Existing methods are computationally prohibitive due to explosive branching and costly branch state update, leading to the trivial worst-case bound $O^{*}\left(2^{n}\right)$ for the basic branch-and-bound baseline wh, where $O^{*}$ suppresses polynomial factors and $n$ is the number of vertices. In this paper, we present an improved method IMinC based on three key ideas: (i) a principled branching state with lineartime update; (ii) a pivot strategy that guides branching toward promising vertices; and (iii) a divide-and-conquer framework that initializes each subproblem to enable our pivot strategy throughout and reduce recursion depth. We further introduce three reduction rules that aggressively prune infeasible branches. Together, these components yield the worst-case time complexity of $O^{*}\left(\alpha_{\ell}^{n}\right)$, where $\alpha_{\ell}$ is a positive number strictly smaller than 2. We also extend IMinC to list minimal $k$-cores under a size bound, addressing practical needs such as size-bounded community search. Extensive experiments on 12 real-world graphs demonstrate that IMinC outperforms the baselines by up to 2 order of magnitude, delivering substantial gains in efficiency.
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