We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.
However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world.
Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements.
Proximity preserving and structural role-based node embeddings became a prime workhorse of applied graph mining.
General purpose graph mining systems provide a generic runtime to explore subgraph structures of interest with the help of user-defined functions that guide the overall exploration process.
GRAPH MINING DISTRIBUTED, PARALLEL, AND CLUSTER COMPUTING DATABASES D.4; H.3.4; H.2.8
To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.
As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network.