CN111428744A - Heterogeneous information network representation learning method and system for reserving type sequence information - Google Patents

Heterogeneous information network representation learning method and system for reserving type sequence information Download PDF

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CN111428744A
CN111428744A CN201910022868.4A CN201910022868A CN111428744A CN 111428744 A CN111428744 A CN 111428744A CN 201910022868 A CN201910022868 A CN 201910022868A CN 111428744 A CN111428744 A CN 111428744A
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陈郁馨
王腾蛟
陈薇
李强
杨旭升
邱镇
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State Grid Corp of China SGCC
State Grid Information and Telecommunication Co Ltd
State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a heterogeneous information network representation learning method and system for reserving type sequence information. The invention provides a heterogeneous information network representation learning model with a reserved type sequence, which is a novel encoder-decoder framework with a type-aware GRU as a gating unit and an attenuation function and can map a heterogeneous information network into a low-dimensional space. The method extends the representation learning of the heterogeneous information network to the sequence level by combining type-aware GRU and sequence information and retaining representative sequence information by using a decay function. The method can support the mapping of the heterogeneous information network to the low-dimensional space, thereby facilitating various network analysis applications such as classification, clustering and visualization of heterogeneous information network data of a social network and the like.

Description

Heterogeneous information network representation learning method and system for reserving type sequence information
Technical Field
The invention belongs to the technical field of information, relates to a deep learning framework, and particularly relates to a heterogeneous information network representation learning method.
Background
The social network is a heterogeneous information network comprising a plurality of types of nodes such as users, blooms and the like and a plurality of types of relationships such as comments, 17-37.) because various types of information in the heterogeneous information network contain abundant information, the representation of the heterogeneous information network is studied and has great significance for studying and researching semantic classification, clustering, prediction, and the like, and the downstream semantic classification and visualization of the data is worthy of being applied to the downstream semantic classification and the like.
The existing heterogeneous information network representation learning method loses rich intrinsic semantic information, particularly type sequence information. The type sequence is a sequence consisting of a node type and a relation type and can express rich semantic relations among nodes. For example, a sequence of types
Figure BDA0001941414230000011
Representing the type of movie that the actor is starring. Based on this type of sequence, the existing method generates a node set for the node Actor1 as a context:
Figure BDA0001941414230000012
Figure BDA0001941414230000013
sequence information has been lost during the context extraction phase. The better context extraction result is a sequence set:
Figure BDA0001941414230000014
Figure BDA0001941414230000015
the existing heterogeneous information network representation learning method lacks a sequence maintaining mechanism, so that the type sequence information cannot be sufficiently combined. Therefore, heterogeneous information network representations that retain type sequence information are quite worthy of study.
The heterogeneous information network representation learning for retaining type sequence information is a challenging task, and has two aspects: 1) how to retain type sequence information: the existing heterogeneous information network representation learning method cannot keep complex type sequence information, so an effective model is needed; 2) which types of sequence information are retained: the theoretically best approach is to retain all types of sequence information in the heterogeneous information network. But the cost grows exponentially as the length of the sequence increases. Therefore, it is quite worthwhile to explore which types of sequence information are retained.
In the past, a plurality of different methods are introduced in the field of Network representation learning research, the classical method comprises projecting a data matrix into a low-dimensional space such as IsoMap, MDS and graph fault, but due to the high complexity of the classical method, researchers have proposed various neural Network-based representation learning methods such as the algorithm of hierarchy learning, B, Al-Rfou, Skaienna, S, Deepwalk on which the Network structure is converted into a linear sequence by random walk of fixed length, and have been constructed by using a Network-map (e.g. map, Qu, M, Yan, J, Qu, Q, J, Q, G, D, K, D, C-D, D-D, C-D, D-D, C, D-D, D-D, C-D.
Disclosure of Invention
In view of the foregoing problems, an object of the present invention is to provide a heterogeneous information network representation learning method, which provides a heterogeneous information network representation learning model with a reserved type sequence to solve the representation learning problem of a heterogeneous information network, thereby facilitating various downstream analysis applications such as classification, clustering, visualization, and the like of heterogeneous information network data of a social network and the like.
The encoder-decoder framework has demonstrated its superiority in handling the sequence-to-sequence prediction problem (seq2seq) in recent years. The Encoder understands the input sequence and creates its low-dimensional vector representation. By inputting the representation result, a decoder generates an output sequence. Wherein, Skip-through (Kiros, r., Zhu, y., salakhitdinov, r.r., Zemel, r., Urtasun, r., Torralba, a., & Fidler, S. (2015). Skip-through vectors, in Advances in processing systems (pp.3294-3302)), an encoder network is used to encode the input sentence, and the decoder network is used to predict the surrounding sentences, thereby obtaining the representation result of the current input sentence. Inspired by the method, in order to learn the representation result of each node in the heterogeneous information network and retain the type sequence information in the process, the method utilizes an encoder-decoder framework to learn the representation result of the heterogeneous information network. According to the invention, each node is taken as an input sequence with the length of 1, and the type sequence information set of the current node is taken as an output sequence set, so that the learned low-dimensional vector representation is the representation result of the type sequence information reserved by the current node. RNN (Recurrent Neural Networks) is the preferred architecture for dealing with sequence problems, and GRU (gated Recurrent Unit) is the common gating mechanism. Aiming at the first challenge (how to keep the type sequence information), the invention designs an RNN encoder-decoder architecture taking type-aware GRU (type-aware GRU) as a gating mechanism to fuse the type information and the sequence information. For the second challenge (which type of sequence information is retained), the present invention considers that representativeness of a type sequence is highly correlated with the distance to the current node. Intuitively, the closer the better. The invention designs a nearest priority strategy and further designs a decay function based on hop count distance (hop-count distance) to retain representative type sequence information.
The heterogeneous information network representation learning model is a novel RNN encoder-decoder framework which takes type-aware GRUs as gate control units and has attenuation functions, and the heterogeneous information network can be mapped into a low-dimensional space. The method extends the representation learning of the heterogeneous information network to the sequence level by combining type-aware GRU and sequence information and retaining representative sequence information by using a decay function. The goal of the model is to find a node representation that is useful for predicting the surrounding type sequence.
Specifically, the invention adopts the following technical scheme:
a heterogeneous information network representation learning method for retaining type sequence information comprises the following steps:
1) generating a type sequence set corresponding to each node in the heterogeneous information network;
2) each node in the heterogeneous information network learns the hidden state through a Type-aware GRU in an encoder and a Type vector of the node, and the hidden state is used as a representation result of the node, so that the current node is mapped to a low-dimensional vector representation result through the encoder;
3) taking the hidden state obtained by the encoder as input, predicting the current element of a Type sequence by using a Type-aware GRU in the decoder in combination with the previous node Type and relationship Type information, and reconstructing the Type sequence of each node by the decoder;
4) combining the type-aware GRUs of each time step by using an attenuation function, and retaining representative type sequence information by adopting a recent priority strategy;
5) optimizing parameters in the encoder and the decoder through continuous iterative computation steps 2) to 4), thereby obtaining a heterogeneous information network representation learning model;
6) inputting the heterogeneous information network to be learned, and obtaining a representation result of each node in the heterogeneous information network to be learned, namely a hidden state of an encoder output of each node through the heterogeneous information network representation learning model in the step 5).
Further, the encoder and decoder are RNN networks using type-aware GRUs as gating units.
Further, in step 2), each node is mapped to a node vector and a type vector by a word2vec method in an encoder.
Further, the Type-aware GRU fuses Type and sequence information into a hidden state, and the Type-aware GRU introduces a matrix
Figure BDA0001941414230000041
And T is a parameter of the type vector, whereby the update gate, the reset gate and the hidden state are calculated in combination with the type information.
Further, in step 3), the hidden state output by the encoder is used as an input by a first type-aware GRU in the decoder, and the first hidden state of the decoder is obtained through calculation, and then the hidden state of the first step of the decoder is obtained through calculation by the node vector and the type vector of the corresponding type sequence element and the hidden state of the previous time step.
Further, step 4) combines the conditional probabilities of each time step by using a decay function, and minimizes a loss function, so that the difference between the predicted type sequence and the real type sequence is minimized; and 5) continuously and iteratively calculating the parameters in the steps 2) -4), thereby obtaining the heterogeneous information network representation learning model.
The invention also provides a heterogeneous information network representation learning system corresponding to the method, which comprises an encoder module and a decoder module, wherein the encoder module and the decoder module both contain Type-aware GRU;
the Type-aware GRU in the encoder is combined with the Type vector of the node to learn the hidden state as the representation result of the node, so that the current node is mapped to a low-dimensional vector representation result;
the Type-aware GRU in the decoder predicts the current element of the Type sequence by combining the previous node Type and relationship Type information, thereby reconstructing the Type sequence of each node;
the decoder combines the type-aware GRUs at each time step by using a decay function, and retains representative type sequence information by adopting a recent priority strategy.
Compared with the prior art, the invention has the following positive effects:
the invention provides a representation learning method of a heterogeneous information network by adopting a representation learning mode of a reserved type sequence, and the heterogeneous information network is mapped into a low-dimensional space. The method can support the mapping of the heterogeneous information network to the low-dimensional space, thereby facilitating various network analysis applications such as classification, clustering and visualization of heterogeneous information network data of a social network and the like.
Drawings
Fig. 1 is a schematic structural diagram of a heterogeneous information network representation learning model of a reserved type sequence according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a Type-aware GRU according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to specific embodiments and accompanying drawings.
First, the meanings of the variables and parameters related to the present invention are explained, as shown in tables 1 and 2.
TABLE 1 variables and their meanings
Figure BDA0001941414230000051
TABLE 2 parameters and their Effect
Figure BDA0001941414230000052
1.Type-aware GRU
Fig. 1 is a schematic diagram of a structure of a heterogeneous information network representation learning model of a reserved type sequence implemented by the present invention. The invention provides a type-aware GRU as a novel gate control unit capable of sensing type information. FIG. 2 is a schematic representation of a Type-aware GRU. The variables and parameters in fig. 1 and 2 have the meanings given in tables 1 and 2, and the arrows above the parameters indicate vectors. Type-aware GRUs fuse Type and sequence information to a hidden state to represent the Type sequence handled so far. The type sequence consists of type sequence elements, wherein the type sequence elements comprise nodes, types of the nodes and relationship types of the nodes and the previous node.
The current hidden state is obtained by the node vector and the type vector of the corresponding type sequence element and the hidden state at the previous time. Wherein the type vector is composed of a node type and a relationship type. The type information also affects the update gate (update gate) and the reset gate (reset gate). The invention introduces the matrix
Figure BDA0001941414230000061
And T is used as a parameter of the type vector, so that the calculation of the updating gate, the resetting gate and the hidden state is realized by combining the type information. Type-aware GRU is defined as follows:
Figure BDA0001941414230000062
Figure BDA0001941414230000063
Figure BDA0001941414230000064
Figure BDA0001941414230000065
wherein,
Figure BDA0001941414230000066
for the reset gate of step i (i.e. Z in figure 2),
Figure BDA0001941414230000067
for the update gate of step i (i.e. u in figure 2),
Figure BDA0001941414230000068
for the hidden state of step I, σ represents a sigmoid function.
2. Attenuation function
The invention designs a recent priority strategy: the closer an element in the type sequence is to the current node, the higher its weight. To adopt this strategy, the attenuation function introduces an attenuation factor with respect to the hop-count distance to reduce the weight of the second half of the sequence.
Let α be the decay factor that controls the decay Rate combine the conditional probability of each time step with the decay function, sequence
Figure BDA0001941414230000069
Is defined as:
Figure BDA00019414142300000610
wherein e-α(l-1)Is a function of attenuation. When l is 1, e-α(l-1)1, does not affect the results. As l increases, the impact on conditional probability decreases exponentially.
3. The method of the invention comprises
1) And generating a corresponding type sequence set for each node in the heterogeneous information network.
And taking each node in the heterogeneous information network as a starting point to perform random walk (with length limit of l) for multiple times in the heterogeneous information network to obtain multiple paths with the length of l taking the current node as the starting point, and taking the paths as a type sequence set.
Further, another optimized method for generating a type sequence set can be adopted: let e-α(l-1)When the sum is less than or equal to 0.01, the influence of the sum on the conditional probability is negligible, namely the hop-count distance is more than or equal to
Figure BDA0001941414230000071
Type sequence element pair conditional probability
Figure BDA0001941414230000072
The effect is negligible. Then the path from which the signal is to be transmitted may be generated as a 1 st, … th,
Figure BDA0001941414230000073
each node is a starting point and has a length of
Figure BDA0001941414230000074
May be used as a type sequence for the start node of the sub-path. From this, a set of type sequences of nodes can be derived.
2) Node viThe node information and the type information are projected by a word2vec method to obtain a node vector xeAnd type vector te. The hidden state of the encoder can be calculated by the type-aware GRU in the encoder, namely the current node viThe low dimension of (2) represents the result.
3) Node viThe result of the representation of (2) is used as the input of the decoder, and the first hidden state of the decoder is calculated by the first type-aware GRU in the decoder. And then the hidden state of the first step of the decoder is calculated by the node vector and the type vector of the corresponding type sequence element and the hidden state of the previous time step. Wherein the node vectors of the type sequence elements and the calculation mode of the type vectors are shown in step 2). And inputting the current hidden state, and obtaining the element of the current step output by the decoder by using a software function.
4) And combining the conditional probabilities of each time step by using a decay function to minimize a loss function so as to minimize the difference between the predicted type sequence and the real type sequence. There are many types of cost functions commonly used at present, such as log loss function and cross entropy loss function.
5) And continuously and iteratively calculating parameters in the steps 2) to 4), so that the type sequence predicted by the model according to the current node and the type information of the current node approaches to the current type sequence in the type sequence set generated in the step 1) every time, namely the loss function is minimum, and the heterogeneous information network representation learning model is obtained. Wherein the attenuation factor needs to be set manually.
6) Inputting the heterogeneous information network to be learned, and obtaining a representation result of each node in the heterogeneous information network to be learned, namely a hidden state of an encoder output of each node through the heterogeneous information network representation learning model in the step 5).
4. The invention further provides a heterogeneous information network representation learning system, which comprises an encoder module and a decoder module, wherein the encoder module and the decoder module both contain Type-aware GRUs;
the Type-aware GRU in the encoder is combined with the Type vector of the node to learn the hidden state as the representation result of the node, so that the current node is mapped to a low-dimensional vector representation result;
the Type-aware GRU in the decoder predicts the current element of the Type sequence by combining the previous node Type and relationship Type information, thereby reconstructing the Type sequence of each node;
the decoder combines the type-aware GRUs at each time step by using a decay function, and retains representative type sequence information by adopting a recent priority strategy.
While the foregoing disclosure shows illustrative embodiments of the invention, it should be noted that various changes and modifications could be made herein without departing from the scope of the invention as defined by the appended claims. In accordance with the structures of the embodiments of the invention described herein, the constituent elements of the claims can be replaced with any functionally equivalent elements. Therefore, the scope of the present invention should be determined by the contents of the appended claims.

Claims (10)

1. A heterogeneous information network representation learning method for retaining type sequence information is characterized by comprising the following steps:
1) generating a type sequence set corresponding to each node in the heterogeneous information network;
2) each node in the heterogeneous information network learns the hidden state through the combination of the type sensing GRU in the encoder and the type vector of the node as the representation result of the node, so that the current node is mapped to a low-dimensional vector representation result;
3) taking the hidden state obtained by the encoder as input, predicting the current element of the type sequence by utilizing the type perception GRU in the decoder in combination with the previous node type and the relation type information, and reconstructing the type sequence of each node;
4) combining the type perception GRUs of each time step by using a decay function, and retaining representative type sequence information by adopting a recent priority strategy;
5) optimizing parameters in the encoder and the decoder through continuous iterative computation steps 2) -4), thereby obtaining a heterogeneous information network representation learning model;
6) inputting the heterogeneous information network to be learned, and obtaining a representation result of each node in the heterogeneous information network to be learned, namely a hidden state of an encoder output of each node through the heterogeneous information network representation learning model in the step 5).
2. The method of claim 1, wherein the encoder and decoder are RNN networks with type-aware GRUs as gating units.
3. The method of claim 1, wherein the type-aware GRU fuses type and sequence information to hidden states, and wherein the type-aware GRU introduces a matrix
Figure FDA0001941414220000011
And T is used as a parameter of the type vector, so that the updating gate, the resetting gate and the hidden state are calculated by combining the type information; the type aware GRU is defined as follows:
Figure FDA0001941414220000012
Figure FDA0001941414220000013
Figure FDA0001941414220000014
Figure FDA0001941414220000015
wherein,
Figure FDA0001941414220000016
in order to reset the gate in the first step,
Figure FDA0001941414220000017
in order to update the gate in the step l,
Figure FDA0001941414220000018
sigma represents a sigmoid function for the hidden state in the step I;
Figure FDA0001941414220000019
parameters representing corresponding node vectors at the time of update gate, reset gate and hidden state calculations;
Figure FDA00019414142200000110
a parameter representing a corresponding previous step length hidden state at the time of the update gate, the reset gate, and the hidden state calculation;
Figure FDA00019414142200000111
a node vector representing the l-1 st element of the type sequence;
Figure FDA00019414142200000112
a type vector representing the l-1 st element of the type sequence;
Figure FDA00019414142200000113
representing the hidden state of the l-1 time step.
4. The method of claim 1, wherein the nearest priority policy is that the element in the type sequence has a higher weight the closer it is to the current node, the decay function introduces a decay factor α with respect to the hop count distance, and combines the conditional probability of each time step with the decay function, the ith node viType sequence of
Figure FDA0001941414220000021
Is defined as:
Figure FDA0001941414220000022
wherein e-α(l-1)In order to be a function of the attenuation,
Figure FDA0001941414220000023
as a sequence of node types
Figure FDA0001941414220000024
L is a type sequence
Figure FDA0001941414220000025
The number of the elements in the (A) is,
Figure FDA0001941414220000026
is the final hidden state of the encoder.
5. The method according to claim 4, wherein step 1) takes each node in the heterogeneous information network as a starting point to perform multiple random walks in the heterogeneous information network, and multiple paths with the length of l taking the current node as the starting point are obtained and used as the type sequence set.
6. The method of claim 5, wherein step 1) generates the first bit from a path of length l
Figure FDA0001941414220000027
Each node is a starting point and has a length of
Figure FDA0001941414220000028
The sub-path is used as the type sequence of the starting node of the sub-path, thereby obtaining the type sequence set of the nodes.
7. The method of claim 1, wherein each node in the heterogeneous information network of step 2) is mapped to a node vector and a type vector by a word2vec method in an encoder.
8. The method of claim 1, wherein the hidden state of step 3) is calculated by a first type-aware GRU in the decoder using the hidden state of the encoder output as input, and the hidden state of the decoder is calculated thereafter by a first step of the decoder from the node vector and the type vector of the corresponding type sequence element and the hidden state of the previous time step.
9. The method according to claim 1, characterized in that step 4) combines the conditional probabilities for each time step with a decay function, minimizing a loss function, minimizing the difference between the predicted type sequence and its true type sequence; and 5) continuously and iteratively calculating the parameters in the steps 2) -4), thereby obtaining the heterogeneous information network representation learning model.
10. A heterogeneous information network representation learning system using the method of claim 1, comprising an encoder module and a decoder module, each of the encoder module and the decoder module including a type-aware GRU;
the type-aware GRU in the encoder learns hidden states in combination with the type vectors of the nodes as a representation of the nodes, thereby mapping the current node to a low-dimensional vector representation;
a type-aware GRU in the decoder predicts a current element of a type sequence in combination with previous node type and relationship type information, thereby reconstructing its type sequence for each node;
the decoder combines the type-aware GRUs at each time step using a decay function, preserving representative type sequence information using a recent precedence strategy.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112182511A (en) * 2020-11-27 2021-01-05 中国人民解放军国防科技大学 Complex semantic enhanced heterogeneous information network representation learning method and device

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112182511A (en) * 2020-11-27 2021-01-05 中国人民解放军国防科技大学 Complex semantic enhanced heterogeneous information network representation learning method and device
CN112182511B (en) * 2020-11-27 2021-02-19 中国人民解放军国防科技大学 Complex semantic enhanced heterogeneous information network representation learning method and device

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Application publication date: 20200717