Gaming-based link predication method and gaming-based link predication system in exchangeable graph

Abstract

The invention discloses a gaming-based link predication method and a gaming-based link predication system in an exchangeable graph. The method comprises the following steps of (1), acquiring a data set, representing elements in the data set through a graph which is composed of a node set and an edge set, wherein the node represents a to-be-predicated entity, and an edge represents a relation between the to-be-predicated entities, and the nodes and the edges of the data set satisfy the requirement of the exchangeable graph; (2), filtering the edges which does not satisfy a preset requirement in the graph by means of a gaming-based network evolution model, and using the filtered graph as the training set; (3), performing iteration training on a probability graph model by means of the training set, and obtaining a probability graph model with an optimal model parameter; and (4), performing link predication by means of the probability graph model with the optimal model parameter in the step (3). The method and the system can be applied in a recommending system, risk evaluation, system programming or social network. The relation between entities satisfies the requirement of the exchangeable graph. Furthermore high speed and high accuracy in relation predication between the entities are realized.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a game-based link prediction method and system in an exchangeable graph. Background technique [0002] In real life, many systems can be modeled by networks whose data structures are graphs. The nodes in the graph represent the entities in the system, and the edges represent the relationships among entities. An exchangeable graph means that in the graph, the order in which edges appear does not affect their distribution, that is, the order of edges is exchangeable. Link prediction (link prediction) refers to the use of known point and edge structures in the network to predict unknown edges, that is, to use the known relationship between entities to predict whether there may be a certain relationship between any two entities. . Link prediction is widely used in recommender systems, risk assessment, and system planning. In commutative graphs, the link prediction problem has no s...

AI Technical Summary

Problems solved by technology

However, since most of these networks are very sparse, there will be a large number of non-existent edges (ie negative samples), which will affect the efficiency and accuracy of model training.

At the same time, some negative sample edges are sometimes not non-existent edges, but may not be observed due to some reasons, such as being missed during observation, etc. These negative samples may immediately establish edges and convert them into positive samples at the next moment , so it is not appropriate to use these as negative samples

On the other hand, in commutative graphs, inference using latent Gaussian processes is generally more complex, less efficient to train, and less parallelizable

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