CN109635989B - Social network link prediction method based on multi-source heterogeneous data fusion - Google Patents

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

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CN109635989B
CN109635989B CN201810999492.8A CN201810999492A CN109635989B CN 109635989 B CN109635989 B CN 109635989B CN 201810999492 A CN201810999492 A CN 201810999492A CN 109635989 B CN109635989 B CN 109635989B
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CN109635989A (en
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周帆
钟婷
吴帮莹
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a social network link prediction method based on multi-source heterogeneous data fusion, which is used for predicting links by utilizing a social network based on geographic position information, wherein the social network comprises a user relationship topological graph and a user check-in record. The invention provides a mixed framework, which fully captures the association between two heterogeneous data sources, namely a user relationship topological graph and a user check-in record in a social network based on geographic position information through a model AL, overcomes the problem of inaccurate prediction result when a single data source in the social network based on the geographic position information is used for link prediction, and effectively improves the link prediction effect. Meanwhile, the calculation speed of deep learning training is increased and the storage overhead is reduced by applying the locality sensitive hashing.

Description

Social network link prediction method based on multi-source heterogeneous data fusion
Technical Field
The invention belongs to the field of neural Networks in machine learning, and relates to a deep learning-Based method, in particular to a method for fusing two heterogeneous data, namely a user relationship topological graph and a user check-in record in a Social network (LBSN) Based on geographical position information by utilizing deep learning to realize Social network link prediction, and improving the calculation speed of deep learning training and reducing the storage cost by using Local Sensitive Hashing (LSH).
Background
Link Prediction (LP), Link Prediction for short, aims to find out a missing edge or an edge which will appear in the future in a user relationship topological graph formed by friend relationships. With the rapid growth of Social Network Services (SNS) and other Network applications, Network data is ubiquitous. Network data such as friend relationships on APPs such as Facebook and QQ are obtained, a user relationship topological graph can be constructed, and the user relationship topological graph can be used for social network link prediction. Meanwhile, with the development of positioning technology, the GPS positioning function of the mobile equipment can be used for collecting the position information of the user, and the position information can be combined with the positioning time to form a user check-in record. Many studies have shown that user check-in records also contribute to social network link prediction.
The link prediction plays an important role in an information recommendation system, is mainly used in social network analysis, can obtain friends with higher confidence degree through the link prediction, recommends the friends possibly known by a user, can remarkably improve the social experience and loyalty of the user, and brings great economic benefits to enterprises. Besides predicting user association in a user relationship topological graph, the link prediction method and the link prediction idea can be used for predicting the type of a label-free node in a network with known partial node types, and the method and the idea have great value for network recombination and structural function optimization.
In the conventional link prediction method, the similarity between two user nodes is usually measured by using a Jaccard, euclidean distance or cosine value, so as to determine whether the link exists. None of these methods is flexible enough. If a new data set is replaced, or data is added or deleted from the original data set, all data needs to be recalculated, and a large amount of computing and storage resources are consumed. The deep learning can flexibly process mass data. The link prediction model built based on the deep learning method can optimize the parameters of the model by inputting massive training data so as to obtain the trained model for prediction work.
Disclosure of Invention
The LBSN data set used by the invention comprises two data sources with different structures, namely a user relation topological graph and a user check-in record. The user relationship topological graph is formed by the relationship among users, the relationship among the users is called as a link (namely a point pair), and each link is formed by the relationship of two user nodes. The user check-in record is composed of a check-in user node, a check-in Point longitude, a check-in Point latitude, a check-in time and a Point-of-Interest (POI).
The invention aims to solve the problem of inaccurate prediction result when a single data source in LBSN is used for link prediction. The basic idea of the invention is to provide a hybrid framework, which fuses two heterogeneous data, namely a user relationship topological graph and a user check-in record in LBSN (location based service), so as to realize link prediction and enhance the prediction effect of the existing link prediction method. And meanwhile, the LSH is adopted to improve the performance of calculation and storage.
Based on the above invention thought, the invention provides a social network link prediction method based on multi-source heterogeneous data fusion, which comprises the following steps:
s1, Data _ process (g) → Tra, Tes: and extracting a training set Tra and a test set Tes from the user relation topological graph G ═ V, E. Wherein V represents the set of user nodes in the topology graph and E represents the set of edges in the topology graph. If two users u in GiAnd ujIf there is a social relationship, there is an edge between them, denoted as eij＝(ui,uj)；
S2，
Figure BDA0001782625390000021
Learning and acquiring a social network user vector V from a positive sample G' of Tra by adopting a network representation learning method, and recording the social network user vector V as
Figure BDA0001782625390000022
Wherein d is
Figure BDA0001782625390000023
Dimension (d);
S3，
Figure BDA0001782625390000024
constructing a user-position check-in frequency matrix according to the user check-in record S ═ U, L
Figure BDA0001782625390000025
Wherein U and L respectively represent a user set and a check-in point set in S, N is the number of users in U, and M is the number of check-in points in L. And then obtaining a user access preference vector in a low-dimensional vector space by using Poisson matrix decomposition, and recording the user access preference vector as
Figure BDA0001782625390000026
Wherein D is
Figure BDA0001782625390000027
Dimension (d);
S4，
Figure BDA0001782625390000028
to capture the association of these two types of data sources in the LBSN, in a manner similar to anchor link (anchor link), an improved deep learning model, called AL, was designed.
Figure BDA0001782625390000029
As a sample, a sample of,
Figure BDA00017826253900000210
as the label corresponding to the sample, the two vectors are input into AL together for multi-round training. Generating a new user access preference vector fused with topology information in G using the final trained AL
Figure BDA00017826253900000211
S5，
Figure BDA0001782625390000031
Will be provided with
Figure BDA0001782625390000032
And ui 'vAnd performing fusion again, and inputting the fused signal into a Convolutional Neural Network (CNN) for training. And finally, inputting the Tes into the trained CNN for link prediction to obtain a prediction result.
In the above method for predicting a social network link based on multi-source heterogeneous data fusion, the step S1 is to obtain Tra and Tes. Link prediction can be viewed as a binary problem, with links present in G being viewed as positive samples and links not present being viewed as negative samples. The positive samples in Tra are the user relationship topological graph G' epsilon G missing part of the links, and the missing links are used as the positive samples of Tes. The method specifically comprises the following steps:
s11, data cleaning is carried out, so that users in G and S in LBSN are consistent;
s12, select some links from G as Tes positive samples. Meanwhile, the G' belonging to G after the Tes positive sample is removed from G is ensured to be communicated; taking G' as a positive sample of Tra;
and S13, randomly selecting some nonexistent links from G as negative samples, and distributing the links into Tra and Tes according to a predefined proportion.
In the above method for predicting social network links based on multi-source heterogeneous data fusion, the step S3 is to obtain
Figure BDA0001782625390000033
The method specifically comprises the following steps:
s31, construction of H using S. Wherein the row of H represents the user, the column represents the POI, and the value of H is filled by the number of times that the user accesses the corresponding POI;
s32, performing Poisson matrix decomposition on H to obtain matrix reflecting user access preference
Figure BDA0001782625390000034
And POI feature matrix
Figure BDA0001782625390000035
The POI feature matrix can reflect the condition that a certain POI is visited by a user. U shapesAs a line of
Figure BDA0001782625390000036
In the above method for predicting the social network link based on multi-source heterogeneous data fusion, the step S4 is to capture the association between G and S in the lbs n, so as to implement fusion. In order to capture such a correlation, the training of the model AL specifically comprises the following sub-steps:
s41, utilizing
Figure BDA0001782625390000037
Representing the user nodes in the Tra, and calculating the cosine mean cos of the user point pairs in the Traori；
S42, capturing the association between G and S by using the one-to-one correspondence of users in V and U. Mixing the sample
Figure BDA0001782625390000038
And corresponding label
Figure BDA0001782625390000039
Dividing the data into a plurality of batches (batch) and circularly inputting the batches into a Multi Layer Performance (MLP) for training;
and S43, optimizing the parameters in the model AL through multiple rounds of training. After AL is trained, will
Figure BDA0001782625390000041
Input AL, output ui 'v。
The implementation method of the model AL described above involves two calculation functions in step S42. The first calculation function is a mapping function for capturing the one-to-one correspondence relationship between users in V and U, and is recorded as
Figure BDA0001782625390000042
The mapping function corresponds to a loss function of
Figure BDA0001782625390000043
Where x represents a sample, y represents a true value, a represents an output value of the model, and n represents the number of samples. A random gradient descent algorithm is called to optimize a global weight parameter W and a global deviation parameter b, and the optimization processes are respectively recorded as
Figure BDA0001782625390000044
Where σ represents the activation function and z is the input to the neuron, expressed as
Figure BDA0001782625390000045
The second calculation function is to ensure that u is generatedi 'vNo offset, i.e. use of ui 'vThe mean value of the cosine of the calculation of the user point pair in the Tra is not less than cosori. Therefore, a cosine mean constraint limit is introduced, noted
Figure 1
Wherein
Figure BDA0001782625390000047
And
Figure BDA0001782625390000048
respectively represent users umAnd user unIs given, and e is present in Gmn. N (U) represents the number of users in U. The cosine mean constraint limits the corresponding loss function to
Figure BDA0001782625390000049
The tuning processes of the global weight parameter W and the global bias parameter b are respectively recorded as
Figure BDA00017826253900000410
Figure BDA00017826253900000411
In the above method for predicting a social network link based on multi-source heterogeneous data fusion, in step S5, G and S are fused again to realize link prediction with low storage consumption and high calculation speed. The method specifically comprises the following steps:
s51, mixing
Figure BDA00017826253900000412
And ui 'vSpliced into a vector
Figure BDA00017826253900000413
S52, applying LSH
Figure BDA0001782625390000051
Projecting into a binary directionQuantity mi∈{0,1}mUp, user uiUsing miRepresents;
s53, for any edge e in Gij＝(ui,uj) Obtaining m by the same methodjAs user ujIs represented by (a);
s54, mixing miAnd mjStitching to obtain edge eijIs represented by a binary vector of mij(∈{0,1}2m)＝[mi；mj]；
S55, converting the vector m with the length of 2mijThe elements in (1) are sequentially filled into a square matrix with the size of n × n in the order of line priority, and the process is called reshaping. Then inputting the square matrix into a Convolutional Neural Network (CNN) for training;
and S56, inputting Tes into the trained CNN for link prediction.
Compared with the prior art, the invention has the following beneficial effects:
1. the method for predicting the social network link based on the multi-source heterogeneous data fusion generates a new user access preference vector fusing the user relationship topological graph by using the model AL similar to the anchor link, and can fully capture the association between the social network user vector and the user access preference vector. The AL can align the social network user vector and the user access preference vector of the same user, and can ensure that a new user access preference vector generated after the user vectors in the two data sources are fused does not shift by introducing a constraint mechanism of cosine mean.
2. The invention discloses a social network link prediction method based on multi-source heterogeneous data fusion, which is characterized in that a social network user vector and a user access preference vector which are respectively obtained by two data sources in an LBSN (location based service) N (location based service) are spliced, the spliced vector is projected onto a binary vector by applying the LSH (least significant Shift H), the binary vector is taken as a final representation vector of a user node in the LBSN, and the final vector of the user node is used for splicing and representing the point-to-point relation according to the point-to-point relation contained in a link in a user relation topological graph. And finally, reshaping the spliced vector into a square matrix, and inputting the square matrix into the CNN to realize link prediction. The application of the LSH can improve the computation speed of deep learning for training and reduce the storage overhead.
Drawings
FIG. 1 is an anchor-like link model AL for capturing associations between social network user vectors and user access preference vectors based on a multi-tier perceptron (MLP).
FIG. 2 is an overall model architecture of a social network link prediction method based on multi-source heterogeneous data fusion. Splicing the new user access preference vector fused with the user relationship topological graph generated in the graph 1 with the social network user vector, projecting the spliced vector to a binary vector by applying an LSH (least squares) algorithm, taking the binary vector as a final representation vector of the user nodes in the LBSN (location based service) N, and splicing the final vector of the user nodes to represent the point-to-point relationship according to the point-to-point relationship contained in the link in the user relationship topological graph. And finally reshaping the spliced vector into a square matrix, and inputting the square matrix into the CNN.
FIG. 3 is a comparison of the performance of the social network link prediction method based on multi-source heterogeneous data fusion when LSH is not used and when LSH is used. Wherein (a) is memory consumption comparison, (b) is CPU consumption comparison, (c) is GPU consumption comparison, and (d) is time consumption comparison. (d) Is the vector dimension input to CNN.
Interpretation of terms
LBSN is an abbreviation for Location-based Social Network, representing a "Location-based Social Network". The LBSN not only contains the contact between people in the traditional social network, but also records the time of the user sign-in, the geographic position and other information.
POI is an abbreviation for Point-of-Interest, representing a "Point of Interest". In lbs n, a POI is a place where a user checks in.
LSH is an abbreviation for local Sensitive Hashing, meaning "Locality Sensitive Hashing". The method is a rapid nearest neighbor search algorithm aiming at massive high-dimensional data.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Examples
The method for predicting the social network link based on the multi-source heterogeneous data fusion can be used for data sets comprising two data sources, namely a user relationship topological graph and a user check-in record. With the real-world LBSN data set shown in Table 1, such as Foursquare (available from Foursquare)http://snap.stanford.eduAcquisition) was performed as an example.
Table 1: relevant information of social link prediction training set for multi-source heterogeneous data fusion
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
Brightkite 75,522 4,038 33,008 1,502
FIG. 1 is an anchor-like link model AL for capturing associations between social network user vectors and user access preference vectors based on a multi-tier perceptron (MLP).
As shown in fig. 1, step S1 is first substituted with the user relationship topology G in Foursquare: data _ process (G) → Tra, Tes, a training set Tra and a test set Tes are acquired. The positive sample G' in Tra is substituted into step S2:
Figure BDA0001782625390000071
wherein the social network user vector can be obtained using a common network learning representation method, such as node2vec
Figure BDA0001782625390000072
Next, step S3 is substituted first using the user check-in record S in Foursquare:
Figure BDA0001782625390000073
obtaining a user access preference vector
Figure BDA0001782625390000074
The outputs of steps S2 and S3
Figure BDA0001782625390000075
And
Figure BDA0001782625390000076
input into model AL, using step S4:
Figure BDA0001782625390000077
obtaining ui 'v。
Table 2: effect of social link prediction on three real data sets
Figure BDA0001782625390000078
FIG. 2 is an overall model architecture of a social network link prediction method based on multi-source heterogeneous data fusion.
As shown in FIG. 2, the outputs of steps S2 and S4
Figure BDA0001782625390000081
And ui 'vInput into the prediction model CNN, using step S5:
Figure BDA0001782625390000082
and acquiring a final prediction result. The link prediction effect of the hybrid model is shown in table 2.
# check _ ins indicates the number of user check-in records;
# POIs indicates the number of different POIs in the user check-in record;
# edges indicates the number of links in the user relationship topology;
# users represents the number of users in the user relationship topology graph (or user check-in record);
foursquare @ NYC represents data in the data set Foursquare with the area of New York City;
foursquare @ TKY represents data in the data set Foursquare with the region of Tokyo;
gowalla @ DC represents data in the data set Gowalla with regions of Washington;
gowalla @ CHI represents data in the data set Gowalla with the region Chicago;
vec2 link-is a method of social network link prediction based on multi-source heterogeneous data fusion without using LSH;
vec2link + is a social network link prediction method based on multi-source heterogeneous data fusion and using LSH, and compared with vec2link-, the method embodies the advantages that after LSH is used, storage occupation is low, and calculation speed is increased;
avage, Hadamard, Weighted-L1, Weighted-L2 are comparative methods of vec2link +, which only use the information of the user relationship topology map in LBSN, and the implementation scheme can be referred to the literature [ Grover, Aditya, and J Leskovec ] "node2vec: Scalable features learning for networks." Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and timing.
Jaccard is a vec2link + comparison method for comparing similarity and difference between finite sample sets;
walk2friend is a comparison method of vec2link +, which uses only data recorded by user check-in lbs n, and the implementation can be found in references [ backs, Michael, et al ] "Walk2friends: involved Social Links from Mobility profiles." Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications security.acm,2017 ].
From the test results in table 2, it can be seen that the prediction effect of the method for predicting the social network link based on the multi-source heterogeneous data fusion is comprehensively superior to that of the method for predicting the link only using a single data source.
Therefore, the method can effectively overcome the problem of inaccurate prediction result when the single data source in the LBSN is used for link prediction, and realizes the improvement of the link prediction effect. The invention adopts a deep learning method to fuse two heterogeneous data sources of a user relationship topological graph and a user check-in record in the LBSN to realize link prediction. And meanwhile, the LSH is adopted, and a discrete binary vector represents a user node, so that the calculation speed of the model is accelerated, and the storage overhead is saved. The invention achieves the purposes of high calculation speed, less storage consumption and better link prediction effect than single-source data prediction effect.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.

Claims (5)

1. A social network link prediction method based on multi-source heterogeneous data fusion is characterized by comprising the following steps:
S1，
Figure 657393DEST_PATH_IMAGE001
: from user relationship topology maps
Figure 974105DEST_PATH_IMAGE002
Extract the training setTra and test setTes; whereinVRepresenting a collection of user nodes in a topological graph,Erepresenting a set of edges in a topological graph; if it isGTwo users inu i Andu j if there is a social relationship, there is an edge between them, which is recorded ase ij =(u i ,u j )；
S2，
Figure 305861DEST_PATH_IMAGE003
: using a web-representation learning method, fromTPositive sample of raG' middle learning and acquisitionVSocial network user vector of
Figure 795748DEST_PATH_IMAGE004
WhereindIs composed of
Figure 60507DEST_PATH_IMAGE005
Dimension (d);
S3，
Figure 485803DEST_PATH_IMAGE006
: according to the user sign-in recordS=(U,L) Building a user-location check-in frequency matrix
Figure 101592DEST_PATH_IMAGE007
(ii) a WhereinUAndLrespectively representSA set of users and a set of check-in points,Nis thatUThe number of users in (1) is,Mis thatLThe number of check-in points in; and then obtaining a user access preference vector in a low-dimensional vector space by using Poisson matrix decomposition, and recording the user access preference vector as
Figure 395170DEST_PATH_IMAGE008
WhereinDIs composed of
Figure 791734DEST_PATH_IMAGE009
Dimension (d);
S4，
Figure 122352DEST_PATH_IMAGE010
: to capture LBSN inGAndSthe association of the two types of data sources adopts a mode of anchor link to design an improved deep learning model called AL;
Figure 225438DEST_PATH_IMAGE009
as a sample, a sample of,
Figure 322707DEST_PATH_IMAGE011
as a label corresponding to the sample, inputting the two vectors into AL together for multi-round training; using the finalTrained AL generation fusesGNew user access preference vector for medium topology information
Figure 765320DEST_PATH_IMAGE012
；
S5，
Figure 657053DEST_PATH_IMAGE013
: will be provided with
Figure 247434DEST_PATH_IMAGE011
And
Figure 758181DEST_PATH_IMAGE014
performing fusion again, inputting the fused data into a Convolutional Neural Network (CNN for short), and training; will eventually beTes is input into the well-trained CNN for link prediction, and a prediction result is obtainedresult。
2. The method for social network link prediction based on multi-source heterogeneous data fusion according to claim 1, wherein the step S1 comprises the following sub-steps:
s11, data cleaning is carried out, so that LBSN is inGAndSthe users in (1) remain consistent;
s12, fromGSelect some links asTes positive samples; while ensuring fromGIs removed fromTAfter es positive sample
Figure 179935DEST_PATH_IMAGE015
Are connected; will be provided withG' asTA positive sample of ra;
s13, fromGRandomly selects some nonexistent links as negative samples and distributes the negative samples to the links according to a predefined proportionTra andTin es.
3. The method for social network link prediction based on multi-source heterogeneous data fusion according to claim 1, wherein the step S3 comprises the following sub-steps:
s31, utilizingSConstruction ofH(ii) a Wherein the content of the first and second substances,Hthe row(s) of (a) represents a user, the column represents a POI,His filled by the number of times the user accesses the corresponding POI;
s32, forHPerforming Poisson matrix decomposition to obtain matrix reflecting user access preference
Figure 180252DEST_PATH_IMAGE016
And POI feature matrix
Figure 195613DEST_PATH_IMAGE017
(ii) a The POI characteristic matrix can reflect the condition that a certain POI is visited by a user;
Figure 634685DEST_PATH_IMAGE018
as a line of
Figure 848628DEST_PATH_IMAGE009
。
4. The method for social network link prediction based on multi-source heterogeneous data fusion according to claim 1, wherein the step S4 comprises the following sub-steps:
s41, utilizing
Figure 957530DEST_PATH_IMAGE019
Show thatTUser node in ra, computeTCosine mean cos of user point pairs in ra ori ；
S42, utilizingVAndUuser one-to-one correspondence to captureGAndSan association between them; mixing the sample
Figure 256924DEST_PATH_IMAGE009
And corresponding label
Figure 234107DEST_PATH_IMAGE011
Dividing into a plurality of batches and circularly inputting into a multilayer perceptron MultilaCarrying out training in yer permission, MLP for short;
s43, adjusting and optimizing parameters in the model AL through multiple rounds of training; after AL is trained, will
Figure 505820DEST_PATH_IMAGE009
Input AL, output
Figure 847939DEST_PATH_IMAGE014
。
5. The method for social network link prediction based on multi-source heterogeneous data fusion according to claim 1, wherein the step S5 comprises the following sub-steps:
s51, mixing
Figure 696947DEST_PATH_IMAGE020
And
Figure 149925DEST_PATH_IMAGE014
spliced into a vector
Figure 541723DEST_PATH_IMAGE021
；
S52, applying LSH
Figure 117061DEST_PATH_IMAGE022
Projecting onto a binary vector
Figure 594310DEST_PATH_IMAGE023
To the useru i Use ofm i Represents;
s53, forGAny one side ofe ij =(u i ,u j ) Obtained by the same methodm j As usersu j Is represented by (a);
s54, mixingm i Andm j splicing to obtainEdgee ij Binary vector representation of
Figure 850979DEST_PATH_IMAGE024
；
S55, setting the length to be 2mVector of (2)m ij The elements in (1) are sequentially filled into a size ofn×nIn the matrix of (a), this process is called remodeling; then inputting the square matrix into a Convolutional Neural Network (CNN) for training;
s56, mixingTes is input into the trained CNN for link prediction.
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