Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Jun Yin

Dafu Deng

Wei Chen

Le Wan

Jiachun Du

Yingjie Li

Xu Cheng

Yong Li

Rui Wang

Juhong Wang

Longjun Sun

Pengpeng Zhao

Zhiyong Wu

Yongkun Li

This disclosure relates to an account filtering method and apparatus. The method includes obtaining user accounts of a social activity network and user activity information; constructing a hypergraph of the social activity network comprising nodes and hyperedges according to the user accounts and the user activity information, wherein a node in the hypergraph comprises the user account in the social activity network, and a hyperedge in the hypergraph comprises a set of user accounts that participates in a user activity of an activity type, or a friendship relation between one user account and another user account; for each node in the hypergraph, calculating a probability that another node of the hypergraph would walk to the node when performing a random walk along the hyperedge in the hypergraph, and obtaining an influence value of the node in the social activity network according to the probability; and sequentially filtering nodes of the hypergraph to obtain a sequence of nodes having a maximum influence by using an iteration algorithm, to form a seed node set.

Social media account filtering method and apparatus

A data processing method includes obtaining sample data of event execution of a game client, and performing preprocessing on the sample data to obtain a plurality of layers of data combinations. Each layer of the plurality of layers of data combinations corresponds to a target event object in a same target event, different layers correspond to different target event objects in the target event, and the target event objects are event objects on the game client to be executed concurrently. The method also includes performing processing on each layer of data combinations according to a preset processing algorithm, to obtain a processing result of each layer of data combinations, and performing consolidation processing on the processing result to obtain a target instruction. The target instruction is used for instructing the game client to concurrently execute the different target event objects corresponding to the different layers of data combinations.

Data processing method and apparatus, and storage medium for concurrently executing event characters on a game client

A graph centrality determining method includes sampling, at least twice, nodes that are sequentially-connected and connection edges between the nodes, in an original graph representing a network structure, to obtain sampled sub-graphs, determining an influence of each of nodes in the sampled sub-graphs, forming a graph centrality determining result of each of the sampled sub-graphs, based on the influence of each of the nodes in the sampled sub-graphs, determining result of each of the sampled sub-graphs, to the original graph, to obtain an influence of each of the nodes in the original graph, clustering the influence of each of the nodes in the original graph, sorting the influence of each of the nodes in the original graph in descending order of a result of the clustering, and obtaining a predetermined quantity of nodes having influences that are top-ranked, in the original graph.

Graph centrality calculation method and apparatus, and storage medium

Embodiments of the present disclosure disclose a method and a device for predicting character behaviors of a non-player character in a game. The method includes receiving status data of a current game sent by a game client, the status data being used to indicate current status of a plurality of characters in the current game; obtaining a behavior order sequence related to the current game from a pre-established behavior order library based on the current status, wherein the behavior order sequence indicates a behavior execution order of the plurality of characters including both player characters and non-player characters; performing simulation on the plurality of characters by using a behavior prediction model in sequence according to the behavior execution order to obtain a simulation result; updating the behavior prediction model according to the simulation result to obtained an updated behavior prediction model; using the updated behavior prediction model to generate behavior information, the behavior information being used to indicate next behaviors of one or more of non-player characters among the plurality of characters; and sending the behavior information to the game client to control the behavior of the one or more non-player characters.

Method and device for generating character behaviors in game and storage medium

Growing community of inventors

Shenzhen, China

Jun Yin