Algorithms for Large-Scale Network Analysis and the NetworKit Toolkit

Abstract

The abundance of massive network data in a plethora of applications makes scalable analysis algorithms and software tools necessary to generate knowledge from such data in reasonable time. Addressing scalability as well as other requirements such as good usability and a rich feature set, the open-source software NetworKit has established itself as a popular tool for large-scale network analysis. This chapter provides a brief overview of the contributions to NetworKit made by the SPP 1736. Algorithmic contributions in the areas of centrality computations, community detection, and sparsification are in the focus, but we also mention several other aspects – such as current software engineering principles of the project and ways to visualize network data within a NetworKit-based workflow.

A Comprehensive Overview and Evaluation of Link Prediction Techniques

“…In Table 1, the implemented hyperparameter values and associated Python packages are detailed. The NetworKit [48] package was used for the heuristic-based link prediction methods. The experiments in this study were conducted on a MacBook Pro equipped with an Apple M1 chip and 8 GB of memory.…”

“…This adopted approach is, to the best of the authors' knowledge, novel. Data preprocessing was performed using a combination of the NetworKit [48] and NetworkX [64] Python packages.…”

HertDroid: Android Malware Detection Method with Influential Node Filter and Heterogeneous Graph Transformer

“…For instance, when we applied an exact betweenness centrality computation to a test subgraph containing 712,687 nodes and 1,263,320 edges, it took more than 10 h to compute. Consequently, we turned to the approximate computation method proposed by Riondato and Kornaropoulos [30], implemented through Networkit [31]. This method employs random sampling of shortest paths to achieve an approximate betweenness centrality computation.…”

“…This ensures that the application nodes in the testing set do not appear in the training or validation sets, thereby preventing potential information leakage. [26] 3.3.5 Decompiled tool Pytorch [43] 1.12.1 Python package Pytorch Geometric [28] 2.1.0.post1 Python package Networkit [31] 10.1 Python package Pandas [44] 1.5.2 Python package Matplotlib [45] 3.5.2 Python package Neo4j [27] 4.4.17 Database…”