CN111126510A - Method for calculating similarity in heterogeneous network and related components thereof - Google Patents
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Abstract
The invention discloses a method for calculating similarity in a heterogeneous network and related components thereof, relating to the field of heterogeneous information networks, wherein the method comprises the following steps: acquiring a type set of each node in a heterogeneous network and a relation set between the nodes of each type; predefining the weight c (R) of the relationship among the nodes of each type; when the similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, acquiring a set of neighbor nodes of the two target nodes in each relationship, and determining the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship. The invention realizes the effect of calculating the similarity without specifying meta path by the user, is easy to use in different data and applications, and adopts a recursive mode for definition, thereby being capable of considering paths with different lengths and semantics, leading the calculated similarity to be capable of integrating more semantic information and leading the quality of the similarity result to be better.
Description
Technical Field
The present invention relates to the field of heterogeneous information networks, and in particular, to a method and an apparatus for calculating similarity in a heterogeneous network, a computer device, and a storage medium.
Background
The heterogeneous network is a graph structure with different node and link types, and there are many nodes in the heterogeneous network, and it is often necessary to calculate the similarity between the nodes so as to perform subsequent operations based on the similarity, such as recommending similar articles, or performing classification, etc. In the prior art, the similarity between nodes is calculated by a similarity calculation method based on meta path, where meta path refers to: the meta path P is a path defined in the network model
For describing the slave node type A1To type AlThe combination-type relationship of (1). Given a meta-path P, there may be multiple paths matching it. However, this way of computation has limitations in heterogeneous networks: firstly, a user is required to provide a lot of additional information, and the user can hardly define a meta path to ensure the similarity query quality; the second, meta path can only capture semantic information of one connection x and y, however, in a heterogeneous network, x and y are often connected by paths with different semantic information. Furthermore, the topology based on the network is recursive, and the number of such paths is theoretically infinite. Therefore, the method based on the metapath cannot aggregate various semantic information, which causes the loss of information when measuring the similarity of the nodes; third, because heterogeneous networks have different types of nodes, meta path defined for calculating the similarity of nodes of a given type cannot be applied to other nodes of different types.
Disclosure of Invention
The embodiment of the invention provides a method and a device for calculating similarity in a heterogeneous network, computer equipment and a storage medium, aiming at improving the accuracy of a similarity calculation result under the condition that a user does not need to provide additional information.
In a first aspect, an embodiment of the present invention provides a method for calculating a similarity in a heterogeneous network, where the method includes:
acquiring a type set of each node in a heterogeneous network and a relation set between the nodes of each type;
predefining the weight c (R) of the relationship among the nodes of each type, wherein R represents the relationship;
when the similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, acquiring a set of neighbor nodes of the two target nodes in each relationship, and determining the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship.
In a second aspect, an embodiment of the present invention provides a device for calculating similarity in a heterogeneous network, including:
the node set acquisition unit is used for acquiring a type set of each node in the heterogeneous network and a relation set between the nodes of each type;
the weight setting unit is used for predefining the weight c (R) of the relationship among the nodes of each type, wherein R represents the relationship;
and the similarity confirming unit is used for acquiring a set of neighbor nodes of the two target nodes in each relationship when the similarity between the two target nodes of the same type in the heterogeneous network needs to be calculated, and determining the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing the computer program, implements the method for calculating similarity in a heterogeneous network according to the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the method for calculating similarity in a heterogeneous network according to the first aspect.
The embodiment of the invention provides a method and a device for calculating similarity in a heterogeneous network, computer equipment and a storage medium. The method comprises the following steps: the method comprises the steps of obtaining a type set of each node in a heterogeneous network and a weight c (R) of a relation between each type of node predefined by a relation set between each type of node, wherein R represents the relation, when the similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, obtaining a set of neighbor nodes of the two target nodes in each relation, and determining the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relation. The method is directly based on the topological structure of the network mode of the heterogeneous network, can calculate the similarity without specifying the metapath by a user, and is easy to use in different data and applications; the method can also aggregate the similarity of different relations, and avoid the defect that one piece of meta can only represent one semantic feature; the method also adopts a recursive mode for definition, so that paths with different lengths and different semantics can be considered, the calculated similarity can integrate more semantic information, and the quality of a similarity result is better.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for calculating similarity in a heterogeneous network according to an embodiment of the present invention;
fig. 2 is an exemplary heterogeneous network diagram of a method for calculating similarity in a heterogeneous network according to an embodiment of the present invention;
fig. 3 is a block diagram of a device for calculating similarity in a heterogeneous network according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a flowchart of a method for calculating similarity in a heterogeneous network according to an embodiment of the present invention.
The specific steps may include S101 to S103:
s101: acquiring a type set of each node in a heterogeneous network and a relation set between the nodes of each type;
a heterogeneous network can be represented by a quadruplet
Where V represents a set of nodes in the heterogeneous network, E represents a set of edges,
is a collection of node types that are,
is a collection of types of edges. In addition, two mapping functions are included
Respectively, to represent the node-to-type, edge-to-type mappings.
There are many types of information in the heterogeneous network, the information is represented in the form of nodes, the nodes have some relationships with each other, and in different relationships, the similarity of two nodes also has different aggregation modes. In the heterogeneous network, each node has different division of labor according to different functions provided, and in this step, a type set of each node and a relationship set between each type of node existing in the heterogeneous network can be obtained.
For example, as shown in fig. 2, there are three types of nodes in a heterogeneous network, namely Paper (Paper), Author (Author), and conference (Venue), and then in the heterogeneous network, the type set of nodes includes three elements: paper, author, and meeting. There are different relationships between these types of nodes, such as a relationship of a paper to an author, a relationship of a paper to a meeting, a relationship of a paper to a paper, which constitute a set of relationships.
In particular, these relationships are directed relationships. For example, the relationship between papers and authors is divided into two categories: the paper is written by the author, and the author writes the paper; the relationship between papers and meetings is also divided into two categories: the thesis is collected in a conference, and the conference collects the thesis; the relationship between papers is also divided into two categories: the paper refers to other papers; the papers are cited by other papers. Thus, there are six directed relationships in the heterogeneous network. Of course, when calculating the similarity between certain types of nodes, it may not be necessary to use all the directed relationships, and several of the relationships may be selected to form the relationship set.
S102: predefining the weight c (R) of the relationship among the nodes of each type, wherein R represents the relationship;
in this step, a weight may be set for the relationship between the nodes of each type, and the weight may be used to determine the similarity between the nodes. For example, to calculate the similarity between two papers, the relationships used include: these two papers are cited in other papers, and both papers are cited in other papers, which authors write the two papers, and which meetings the two papers are included in. A weight can be set for each of these four relationships: 30% on the similarity of the papers they refer to, 20% on the similarity of the meetings they are included in, 20% on the similarity of their authors.
S103: when the similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, acquiring a set of neighbor nodes of the two target nodes in each relationship, and determining the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship.
When the similarity between two target nodes needs to be obtained, the neighbor nodes of the two target nodes in each relationship need to be counted first, and because the similarity of the two target nodes depends on the similarity of the neighbor nodes, the similarity of the target nodes is determined according to the similarity of the neighbor nodes of the two target nodes in each relationship in the embodiment of the invention.
The embodiment of the invention introduces the concept of the escape Function, which is used for defining the aggregation mode of the similarity from the neighbor nodes, namely for describing how the similarity of the nodes a and b depends on the similarity of different types of neighbor nodes.
Decay Function given a network mode
A Decay function c is defined in
And assigning each relation R toThe value is a positive real number (used for describing the proportion of the relation in the node similarity aggregation), and simultaneously satisfies the following conditions:
1、
the proportion of each relation in the calculation of the node similarity is non-negative.
2、
The ratio of the similarity representing all the neighbor nodes to the similarity calculation of the original node cannot exceed 100 percent.
In an embodiment, the step S103 further includes:
calculating the similarity s (a, b) of the two target nodes a and b according to the following formula:
in the formula (I), the compound is shown in the specification,
set of relationships representing a node type A, NR(a) Representing the set of neighbor nodes of the target node a in the relation R, NR(b) Represents the set of neighbor nodes of the target node b in the relationship R, | NR(a) | represents the set NR(a) The number of middle nodes, | NR(b) | represents the set NR(b) The number of intermediate nodes.
In the present embodiment, a calculation formula for calculating the similarity between two target nodes a and b is given, where c (r) is the weight preset in step S102, S (a ', b') is the similarity between nodes, and S (a ', b') in the formula always ensures that the types of a 'and b' are the same. Wherein N isR(a) Representing the set of neighbor nodes of the target node a in the relationship R, as shown in FIG. 2, assuming that R represents the relationships that the paper was written by which authors, then NR(P1)＝{x1,x2,x3Denotes paper P1By author x1,x2And x3Composition, NR(P2)＝{x1,x2,x3Denotes paper P2By author x1,x2And x3And (5) writing.
Further, in the set of relationships with the node type a, the sum of the weights of the relationships is 100%.
When the weights are preset, the sum of the weights of all the relations in the relation set is 100%. When setting the weight, different weights can be set according to different importance degrees of various relationships in the relationship set. For example, in the foregoing embodiment, the relationship of the paper referenced by the target node is important, so a higher weight can be set.
Further, if the two target nodes a and b are the same node, the similarity s (a, b) between a and b is 1.
When the acquired nodes a and b are the same node, the formula s (a, b) is 1, which means that the similarity between one node and itself is 1.
Further, the type of the node is greater than or equal to 3. For example, in the foregoing embodiment, the types of nodes include three types, namely author, paper and conference, but in other heterogeneous networks, different numbers of types of nodes may be included, for example, the types of nodes may be 2, 4 or 5, and so on.
Referring to fig. 3, fig. 3 is a block diagram illustrating a computing device 300 for similarity in a heterogeneous network according to an embodiment of the present invention;
the apparatus 300 may include:
a node set obtaining unit 301, configured to obtain a type set of each node in the heterogeneous network and a relationship set between the type nodes;
a weight setting unit 302, configured to pre-define a weight c (R) of a relationship between each type of node, where R represents the relationship;
a similarity determining unit 303, configured to, when similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, obtain a set of neighbor nodes of the two target nodes in each relationship, and determine the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship.
In one embodiment, the similarity determination unit 303 includes:
a first similarity calculation unit for calculating a similarity s (a, b) of the two target nodes a and b according to the following formula:
in the formula (I), the compound is shown in the specification,
set of relationships representing a node type A, NR(a) Representing the set of neighbor nodes of the target node a in the relation R, NR(b) Represents the set of neighbor nodes of the target node b in the relationship R, | NR(a) | represents the set NR(a) The number of middle nodes, | NR(b) | represents the set NR(b) The number of intermediate nodes.
In an embodiment, in the set of relationships with node type a, the sum of the weights of the relationships is 100%.
In an embodiment, the similarity confirming unit 303 further includes:
and a second similarity calculation unit, configured to, if the two target nodes a and b are the same node, set a similarity s (a, b) of a and b to 1.
In an embodiment, the type of the node is greater than or equal to 3.
In one embodiment, the relationship is a directional relationship.
Since the embodiments of the apparatus portion and the method portion correspond to each other, please refer to the description of the embodiments of the method portion for the embodiments of the apparatus portion, which is not repeated here.
The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed, can implement the method provided by the above-described embodiments.
The invention also provides a computer device, which may include a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method provided by the above embodiments when executing the computer program.
The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (9)
1. A method for calculating similarity in a heterogeneous network is characterized by comprising the following steps:
acquiring a type set of each node in a heterogeneous network and a relation set between the nodes of each type;
predefining the weight c (R) of the relationship among the nodes of each type, wherein R represents the relationship;
when the similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, acquiring a set of neighbor nodes of the two target nodes in each relationship, and determining the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship.
2. The method according to claim 1, wherein when the similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, acquiring a set of neighbor nodes of the two target nodes in each relationship, and determining the similarity between the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship, comprises:
calculating the similarity s (a, b) of the two target nodes a and b according to the following formula:
in the formula (I), the compound is shown in the specification,
set of relationships representing a node type A, NR(a) Representing the set of neighbor nodes of the target node a in the relation R, NR(b) Represents the set of neighbor nodes of the target node b in the relationship R, | NR(a) | represents the set NR(a) The number of middle nodes, | NR(b) | represents the set NR(b) The number of intermediate nodes.
3. The method according to claim 2, wherein the sum of weights of relationships in the set of relationships with node type a is 100%.
4. The method according to claim 2, wherein when the similarity between two target nodes of the same type in the heterogeneous network needs to be calculated, the method obtains a set of neighbor nodes of the two target nodes in each relationship, and determines the similarity between the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship, further comprising:
if the two target nodes a and b are the same node, the similarity s (a, b) between a and b is 1.
5. The method of claim 2, wherein the type of the node is greater than or equal to 3.
6. The method of claim 2, wherein the relationship is a directed relationship.
7. An apparatus for computing similarity in a heterogeneous network, comprising:
the node set acquisition unit is used for acquiring a type set of each node in the heterogeneous network and a relation set between the nodes of each type;
the weight setting unit is used for predefining the weight c (R) of the relationship among the nodes of each type, wherein R represents the relationship;
and the similarity confirming unit is used for acquiring a set of neighbor nodes of the two target nodes in each relationship when the similarity between the two target nodes of the same type in the heterogeneous network needs to be calculated, and determining the similarity of the two target nodes according to the similarity between the neighbor nodes of the two target nodes in each relationship.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of calculating similarity in a heterogeneous network according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, implements the method of calculating similarity in heterogeneous networks according to any one of claims 1 to 6.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111476322A (en) * | 2020-05-22 | 2020-07-31 | 哈尔滨工程大学 | Meta-path mining method based on feature optimization |
| CN111597396A (en) * | 2020-05-13 | 2020-08-28 | 深圳计算科学研究院 | Heterogeneous network community detection method and device, computer equipment and storage medium |
| CN113746652A (en) * | 2020-05-29 | 2021-12-03 | 华为技术服务有限公司 | Method and device for determining main/standby protection pairs |
| WO2022141655A1 (en) * | 2020-12-30 | 2022-07-07 | 深圳计算科学研究院 | Method for distributed-type computation of simrank single source node similarity measure, and apparatus |
| CN115564013A (en) * | 2021-08-09 | 2023-01-03 | 中山大学 | Method for improving network representation learning representation capability, model training method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104090957A (en) * | 2014-03-10 | 2014-10-08 | 中国科学院软件研究所 | Heterogeneous network interactive visualization method |
| CN110580491A (en) * | 2018-06-11 | 2019-12-17 | 广东神马搜索科技有限公司 | Node similarity calculation method, device, equipment and computer readable storage medium |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7296011B2 (en) * | 2003-06-20 | 2007-11-13 | Microsoft Corporation | Efficient fuzzy match for evaluating data records |
| US10691743B2 (en) * | 2014-08-05 | 2020-06-23 | Sri International | Multi-dimensional realization of visual content of an image collection |
| CN104008165B (en) * | 2014-05-29 | 2017-05-24 | 华东师范大学 | Club detecting method based on network topology and node attribute |
-
2020
- 2020-01-02 CN CN202010000760.8A patent/CN111126510A/en active Pending
- 2020-01-08 WO PCT/CN2020/070782 patent/WO2021134807A1/en active Application Filing
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104090957A (en) * | 2014-03-10 | 2014-10-08 | 中国科学院软件研究所 | Heterogeneous network interactive visualization method |
| CN110580491A (en) * | 2018-06-11 | 2019-12-17 | 广东神马搜索科技有限公司 | Node similarity calculation method, device, equipment and computer readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| GLEN JEH等: "SimRank: A Measure of Structural-Context Similarity", 《PROCEEDINGS OF THE EIGHTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111597396A (en) * | 2020-05-13 | 2020-08-28 | 深圳计算科学研究院 | Heterogeneous network community detection method and device, computer equipment and storage medium |
| CN111476322A (en) * | 2020-05-22 | 2020-07-31 | 哈尔滨工程大学 | Meta-path mining method based on feature optimization |
| CN111476322B (en) * | 2020-05-22 | 2022-05-20 | 哈尔滨工程大学 | Meta-path mining method based on feature optimization |
| CN113746652A (en) * | 2020-05-29 | 2021-12-03 | 华为技术服务有限公司 | Method and device for determining main/standby protection pairs |
| CN113746652B (en) * | 2020-05-29 | 2023-12-19 | 华为技术服务有限公司 | Method and device for determining active-standby protection pairs |
| WO2022141655A1 (en) * | 2020-12-30 | 2022-07-07 | 深圳计算科学研究院 | Method for distributed-type computation of simrank single source node similarity measure, and apparatus |
| CN115564013A (en) * | 2021-08-09 | 2023-01-03 | 中山大学 | Method for improving network representation learning representation capability, model training method and system |
| CN115564013B (en) * | 2021-08-09 | 2024-02-09 | 中山大学 | Method for improving learning representation capability of network representation, model training method and system |
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