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Social network link prediction method based on multi-source heterogeneous data fusion

A multi-source heterogeneous data, social network technology, applied in forecasting, data processing applications, neural learning methods, etc., can solve problems such as inaccurate forecast results

Active Publication Date: 2019-04-16
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the problem of inaccurate prediction results when using a single data source in LBSN for link prediction

Method used

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  • Social network link prediction method based on multi-source heterogeneous data fusion
  • Social network link prediction method based on multi-source heterogeneous data fusion
  • Social network link prediction method based on multi-source heterogeneous data fusion

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Embodiment

[0045] The method for predicting social network links based on multi-source heterogeneous data fusion provided by this implementation can be used for data sets that include two data sources: user relationship topology map and user check-in records. Take the real-world LBSN dataset shown in Table 1, such as Foursquare (available from http: / / snap.stanford.edu Acquisition) as an example for experiments.

[0046] Table 1: Relevant information of the social link prediction training set for multi-source heterogeneous data fusion

[0047] Dataset

#check_ins

#POIs

#edges

#users

Foursquare@NYC

22,563

1,992

5,810

588

Foursquare@TKY

38,742

2,212

9,624

1,055

Gowalla@DC

13,594

4,795

5,826

880

Gowalla@CHI

10,314

3,269

2,542

627

Bright kite

75,522

4,038

33,008

1,502

[0048] figure 1 An anchor-like link model AL that captures the association betwe...

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Abstract

The invention discloses a social network link prediction method based on multi-source heterogeneous data fusion. Link prediction is carried out by using a social network based on geographic position information, wherein the social network comprises two heterogeneous data sources, namely a user relationship topological graph and a user sign-in record. The invention provides a mixed frame. The association between two heterogeneous data sources, namely a user relationship topological graph and a user sign-in record, in a social network based on geographic position information is fully captured through a model AL. The problem that the prediction result is inaccurate when a single data source in a social network based on geographic position information is used for link prediction is solved, andthe link prediction effect is effectively improved. And meanwhile, local sensitive hashing is applied to improve the calculation speed of deep learning for training and reduce the storage overhead.

Description

technical field [0001] The present invention belongs to the neural network field in machine learning, is a kind of method based on deep learning, especially utilizes deep learning to be based on the social network (Local Based Social Networks, LBSN) of geographic location information user relationship topological map and user check-in Record these two kinds of heterogeneous data for fusion to realize social network link prediction, and use Locality Sensitive Hashing (LSH) to improve the calculation speed of deep learning for training and reduce storage overhead. Background technique [0002] Social network link prediction (Link Prediction, LP), referred to as link prediction, aims to find out the missing edges in the graph or the edges that will appear in the future from a user relationship topology graph composed of friend relationships. With the rapid growth of social network services (Social Network Service, SNS) and other network applications, network data is ubiquitous....

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/00G06N3/08
CPCG06Q10/04
Inventor 周帆钟婷吴帮莹
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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