GraphSL: An Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets
We introduce GraphSL, a new library for studying the graph source localization problem. graph diffusion and graph source localization are inverse problems in nature: graph diffusion predicts information diffusions from information sources, while graph source localization predicts information sources from information diffusions. GraphSL facilitates the exploration of various graph diffusion models for simulating information diffusions and enables the evaluation of cutting-edge source localization approaches on established benchmark datasets. The source code of GraphSL is made available at Github Repository (https://github.com/xianggebenben/GraphSL). Bug reports and feedback can be directed to the Github issues page (https://github.com/xianggebenben/GraphSL/issues).
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification
Sleep stage classification is essential for sleep assessment and disease diagnosis. However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging. In…
Automatic Sleep Stage ClassificationClassificationEEGElectroencephalogram (EEG)+2LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained la…
Graph Representation LearningKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge Graphs+5LIBTwinSVM: A Library for Twin Support Vector Machines
This paper presents LIBTwinSVM, a free, efficient, and open source library for Twin Support Vector Machines (TSVMs). Our library provides a set of useful functionalities such as fast TSVMs estimators, model selection, vi…
General ClassificationModel SelectionPenman: An Open-Source Library and Tool for AMR Graphs
Abstract Meaning Representation (AMR) (Banarescu et al., 2013) is a framework for semantic dependencies that encodes its rooted and directed acyclic graphs in a format called PENMAN notation. The format is simple enough …
Abstract Meaning RepresentationA Library for Representing Python Programs as Graphs for Machine Learning
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to construct graph representat…