Dgraph: large scale dynamic graph dataset

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Webpage: https://dgraph.xinye.com/

GitHub: https://github.com/DGraphXinye/

In recent days, , Yang Yang's scientific research group of Zhejiang University （yangy.org） Hexin also jointly released a large-scale dynamic graph data set DGraph, Aimed at service graph neural network 、 Graph mining 、 Social networks 、 Researchers in the direction of anomaly detection , Provide large-scale data of real scenes .DGraph On the one hand, it can be used as the standard data to verify the performance of the correlation graph model , On the other hand, it can also be used to carry out user portrait 、 Network analysis and other research work .

Data set description  

DGraph The source data of is provided by Xinye Technology .DGraph It is a directed dynamic graph with no right , Contains more than 370 Ten thousand nodes and 430 Ten thousand dynamic edges . As shown in the figure below ,DGraph The node in represents the financial lending user of Xinye technology service , A directed edge indicates an urgent contact relationship , Each node contains the attribute characteristics after desensitization , And a label indicating whether it is a financial fraud user .

Data features

The scene is real

DGraph It comes from the real financial business scenario , Its construction logic is close to the industrial landing , It provides an opportunity for users of data sets to explore how to extend the graph model to the financial field . To be specific ,DGraph The proportion of abnormal and normal users in is about 1:100, Its “ The label is unbalanced ” The characteristics of the are in line with the real scene , Support exception detection 、 Research on classification of unbalanced nodes .

Structural dynamics

DGraph User relationships in are sampled from across 27 A business scenario for months , And the network structure will evolve over time , It provides data support for the current dynamic graph model and mining research .

Large scale

DGraph contain 370 Thousands of desensitized real financial lending users and 430 Ten thousand dynamic relationships , Its scale is about the largest dynamic graph data in the financial field Elliptic Of 17 times , Support the research and evaluation of large-scale graph models . Besides ,DGraph Contained in the 60% Of “ Background node ”, That is, it is not a classification or analysis object, but it actually exists 、 Nodes that have an indirect impact on business logic . These nodes play an important role in maintaining the connectivity of the network , Widely exists in industry . Reasonable processing of background nodes can effectively improve the storage space of data and the operation efficiency of the model in large-scale data scenarios .DGraph It contains more than 200 10000 background nodes , It can support researchers to explore the properties of background nodes .

Open source community maintenance

Ranking List

DGraph Users can submit at any time 、 Refreshed performance leaderboard （leaderboard）, To track the research progress of the latest graph model . The list provides a unified evaluation process , All results are open and transparent .

Research results

DGraph It has rich characteristics , Support graph research in multiple directions .

Algorithm contest

Xinye technology revolves around DGraph The seventh Xinye Technology Cup algorithm competition was held , Task and DGraph The fraud user identification in is consistent . The competition is open to the whole society , Colleges and universities at home and abroad 、 Scientific research institutes 、 Internet enterprises can sign up for the competition , The bonus pool is abundant , total 31 Thousands of yuan .

Welcome interested colleagues 「 Scan the qr code below 」 Patronize DGraph Public data website , Work together to provide rich application data for the field of artificial intelligence , Work together to build an open digital ecosystem .

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Related papers ：

DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection. Xuanwen Huang, Yang Yang*, Yang Wang, Chunping Wang, Zhisheng Zhang, Jiarong Xu, and Lei Chen. Preprint.

Thesis link ：

http://yangy.org/works/dgraph/dgraph_2022.pdf

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