CN111125547A - Knowledge community discovery method based on complex network - Google Patents
Knowledge community discovery method based on complex network Download PDFInfo
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Abstract
The invention discloses a knowledge community discovery method based on a complex network, which comprises the steps of selecting, marking a set, community division, substitution calculation, uploading construction, instruction search, comparison transmission, substitution comparison and result output, and is safe and convenient to use, firstly selecting an inventory knowledge network and a knowledge network in the Internet for comparison to obtain a complex knowledge network node set, then selecting the complex knowledge network, marking all nodes of the complex knowledge network to obtain a node set of the complex knowledge network, dividing the existing knowledge community into a plurality of knowledge communities, then substituting the complex knowledge network node set into the knowledge communities for calculation, dividing the complex knowledge network nodes into corresponding knowledge communities, dividing the complex knowledge network nodes into each knowledge community, and having higher efficiency of dividing the complex knowledge network nodes into each knowledge community, the relevance of the divided complex knowledge network nodes to the knowledge community is higher.
Description
Technical Field
The invention relates to the technical field of complex network analysis, in particular to a knowledge community discovery method based on a complex network.
Background
With the rapid development of computer technology and the Internet, people have stronger and stronger storage and processing capacity on real network data, and on the basis, the complex network is discovered to have certain structural characteristics and functional characteristics, and as important characteristics in the complex network, a community structure is visible everywhere in the real network, wherein a knowledge community can be defined as: the method comprises the steps that a part of people gather the common interest and knowledge acquisition and communication requirements of a certain theme, and carry out the activities of creating and sharing related knowledge to form a group with close interaction relation, although a subject defined by a knowledge community is a group, the subject defined by the knowledge community broadly covers the factors of members gathered by the group, a platform for group communication, produced knowledge, a group maintaining mechanism and the like, the gathering of the knowledge community is due to the common attention to the same theme and the knowledge requirements caused by the common attention to the same theme, which means that the knowledge community usually has a certain keyword or consists of a plurality of groups with keywords, and the network knowledge community is formed by the keywords or the topics and is the core of the network knowledge community;
the current method for discovering the community structure in the complex network mainly comprises the following steps: the method comprises a graph partitioning method, a splitting method, an aggregation method, an index optimization-based method and the like, but at present, because the data volume of a knowledge community is large, and the network structure formed in the knowledge community is connected more, when the knowledge community with the large data volume is analyzed by the conventional method for discovering the community structure in a complex network, the required calculated amount is very large, the analysis time of the knowledge community is long, and the efficiency and the effect of analyzing and discovering the knowledge community are easily influenced, so that the knowledge community discovering method of the complex network needs to be designed, and the knowledge community of the complex network is analyzed and discovered.
Disclosure of Invention
The technical scheme provided by the invention can effectively solve the problems that the prior method for discovering the community structure in the complex network, which is provided by the background technology, needs extremely large amount of calculation when analyzing the knowledge community with large data volume, and the analysis time of the knowledge community is long, so that the analysis and discovery efficiency and effect of the knowledge community are easily influenced.
In order to achieve the purpose, the invention provides the following technical scheme: a knowledge community discovery method based on a complex network comprises the following steps:
s1, selecting: locally selecting a stock knowledge network and a knowledge network in the Internet for comparison to obtain a complex knowledge network;
s2, label set: selecting a complex knowledge network, and marking nodes of the complex knowledge network to obtain a node set of the complex knowledge network;
s3, community division: selecting internal nodes of the existing knowledge community, substituting the complex knowledge network nodes into the knowledge community respectively, and dividing the existing knowledge community into a plurality of knowledge communities;
s4, substitution calculation: substituting the obtained complex knowledge network node set into a plurality of divided knowledge communities, and dividing the complex knowledge network nodes into corresponding knowledge communities;
s5, uploading and constructing: selecting the divided knowledge communities and the complex knowledge network node sets divided according to the corresponding knowledge communities, uploading and storing the divided knowledge communities and the complex knowledge network node sets in a cloud server, and constructing a search engine according to the divided knowledge communities and the complex knowledge network node sets;
s6, command search: receiving a search instruction input in a search engine of a user side, converting characters of the search instruction into binary codes, and transmitting the binary codes to a cloud server;
s7, comparison and transmission: comparing the binary codes of the search instruction transmitted into the cloud server with the complex knowledge network nodes in the cloud server to obtain the network nodes of the search instruction;
s8, substitution comparison: substituting the obtained network nodes of the search instruction into the divided knowledge communities in the cloud server, and comparing to obtain the knowledge communities corresponding to the network nodes of the search instruction;
s9, outputting the result: and transmitting the obtained knowledge community corresponding to the search instruction network node to a user terminal, and outputting and displaying.
According to the technical characteristics, in the step S1, an inventory knowledge network is selected from the local and compared with knowledge networks in the internet one by one, the inventory knowledge network refers to knowledge data stored in the local host hard disk, repeated knowledge network contents are removed, and the remaining knowledge network contents are unified and sorted to obtain a complex knowledge network.
According to the technical characteristics, in the step S2, the complex knowledge network is selected, and all nodes of the complex knowledge network are marked according to the difference of the relative positions of the nodes of the complex knowledge network, so as to obtain a node set of the complex knowledge network.
According to the technical characteristics, in the step S3, the algorithm for community division substitution is
Wherein, yijIs the number of internal node pairs of the community,
is the total number of network nodes, the larger the result value of R (Y, Y'), the more reasonable the community partition.
According to the technical features, in step S4, the algorithm for substituting the complex knowledge network node into the knowledge community is as follows:
Sira matrix defined as n r, n being the number of nodes, r being the number of communities, where B is a modular matrix whose elements are:
the possible space of community division is searched, the community division of the maximum Q value can be obtained, and the substituted complex knowledge network nodes are divided according to the corresponding knowledge communities.
According to the above technical features, in step S4, if the node i belongs to the community r, it is 1, otherwise, it is 0, and there are:
δ(ci,cj)＝∑SirSjrj，
δ(ci,cj) Refers to the correlation, δ (c), between node i and community ri,cj) The larger the value of (d), the greater the correlation of the node i with the community r.
According to the technical characteristics, in the step S5, the divided knowledge communities and the complex knowledge network node sets divided according to the corresponding knowledge communities are selected, each knowledge community and the corresponding complex knowledge network node set are marked, all the marked knowledge communities and the complex knowledge network node sets are uploaded and stored in the cloud server, and a search engine is constructed according to the marked knowledge communities and the complex knowledge network node sets.
According to the above technical features, in step S6, the cloud server receives a search instruction input in the user-side search engine, converts text information of the search instruction into a binary code corresponding to the complex knowledge network, and transmits the binary code to the cloud server.
According to the technical characteristics, in the step S7, the binary codes of the search instruction transmitted to the cloud server are compared with the complex knowledge network nodes in the cloud server one by one to obtain the network nodes of the search instruction, and the binary codes of the search instruction transmitted to the cloud server are compared with the complex knowledge network nodes in the cloud server one by one again to be compared with the network nodes of the search instruction obtained for the first time, if the binary codes are the same, the search instruction is output, and if the binary codes are different, the search instruction is compared again.
According to the technical characteristics, in the step S8, substituting the obtained network nodes of the search instruction into the partitioned knowledge communities in the cloud server to perform one-by-one comparison, so as to obtain the knowledge communities corresponding to the network nodes of the search instruction, substituting the obtained network nodes of the search instruction into the partitioned knowledge communities in the cloud server again to perform one-by-one comparison, comparing the obtained network nodes with the comparison result obtained for the first time, and if the obtained network nodes are the same, outputting the comparison result, otherwise, performing the comparison again.
Compared with the prior art, the invention has the beneficial effects that:
firstly, a stock knowledge network and knowledge networks in the Internet are selected locally to be compared one by one, repeated knowledge network contents are removed, the remaining knowledge network contents are unified and arranged to obtain a complex knowledge network, then the complex knowledge network is selected, all nodes of the complex knowledge network are marked according to the difference of the relative positions of the nodes of the complex knowledge network to obtain a node set of the complex knowledge network, then the internal nodes of the existing knowledge community are selected, the nodes of the complex knowledge network are substituted into the knowledge community to be calculated, the existing knowledge community is divided into a plurality of knowledge communities, the division accuracy of the knowledge community is better, and the reasonable degree of the community division can be visually displayed by data;
then substituting the complex knowledge network node set into the knowledge community to calculate, dividing the complex knowledge network nodes into the corresponding knowledge communities, dividing the complex knowledge network nodes into each knowledge community, wherein the efficiency of dividing the complex knowledge network nodes into each knowledge community is higher, and the correlation between the divided complex knowledge network nodes and the knowledge community is higher;
the search instruction binary codes can be repeatedly compared with complex knowledge network nodes in the cloud server to obtain accurate numerical values of the search instruction network nodes, the obtained search instruction network nodes can be repeatedly compared with the divided knowledge communities to obtain accurate data of the knowledge communities corresponding to the search instruction network nodes, the comparison effect of the search instruction network nodes and the divided knowledge communities is better, and the situations of calculation errors are fewer.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a schematic view of the flow structure of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example (b): as shown in fig. 1, the invention provides a technical solution, and a knowledge community discovery method based on a complex network, comprising the following steps:
s1, selecting: locally selecting a stock knowledge network and a knowledge network in the Internet for comparison to obtain a complex knowledge network;
s2, label set: selecting a complex knowledge network, and marking nodes of the complex knowledge network to obtain a node set of the complex knowledge network;
s3, community division: selecting internal nodes of the existing knowledge community, substituting the complex knowledge network nodes into the knowledge community respectively, and dividing the existing knowledge community into a plurality of knowledge communities;
s4, substitution calculation: substituting the obtained complex knowledge network node set into a plurality of divided knowledge communities, and dividing the complex knowledge network nodes into corresponding knowledge communities;
s5, uploading and constructing: selecting the divided knowledge communities and the complex knowledge network node sets divided according to the corresponding knowledge communities, uploading and storing the divided knowledge communities and the complex knowledge network node sets in a cloud server, and constructing a search engine according to the divided knowledge communities and the complex knowledge network node sets;
s6, command search: receiving a search instruction input in a search engine of a user side, converting characters of the search instruction into binary codes, and transmitting the binary codes to a cloud server;
s7, comparison and transmission: comparing the binary codes of the search instruction transmitted into the cloud server with the complex knowledge network nodes in the cloud server to obtain the network nodes of the search instruction;
s8, substitution comparison: substituting the obtained network nodes of the search instruction into the divided knowledge communities in the cloud server, and comparing to obtain the knowledge communities corresponding to the network nodes of the search instruction;
s9, outputting the result: and transmitting the obtained knowledge community corresponding to the search instruction network node to a user terminal, and outputting and displaying.
According to the technical characteristics, in step S1, an inventory knowledge network is selected from the local and compared with knowledge networks in the internet one by one, the inventory knowledge network refers to knowledge data stored in the local host hard disk, repeated knowledge network contents are removed, and the remaining knowledge network contents are unified and sorted to obtain a complex knowledge network.
According to the technical characteristics, in step S2, a complex knowledge network is selected, and all nodes of the complex knowledge network are marked according to the difference of the relative positions of the nodes of the complex knowledge network, so as to obtain a node set of the complex knowledge network.
According to the above technical features, in step S3, the algorithm for community partition substitution is
Wherein, yijIs the number of internal node pairs of the community,
is the total number of network nodes, the larger the result value of R (Y, Y'), the more reasonable the community partition.
According to the technical characteristics, in step S4, the algorithm for substituting the complex knowledge network node into the knowledge community is as follows:
Sira matrix defined as n r, n being the number of nodes, r being the number of communities, where B is a modular matrix whose elements are:
the possible space of community division is searched, the community division of the maximum Q value can be obtained, and the substituted complex knowledge network nodes are divided according to the corresponding knowledge communities.
According to the above technical features, in step S4, if the node i belongs to the community r, it is 1, otherwise, it is 0, and there are:
δ(ci,cj)＝∑SirSjrj，
δ(ci,cj) Refers to the correlation, δ (c), between node i and community ri,cj) The larger the value of (d), the greater the correlation of the node i with the community r.
According to the technical characteristics, in step S5, the divided knowledge communities and the complex knowledge network node sets divided according to the corresponding knowledge communities are selected, each knowledge community and the corresponding complex knowledge network node set are marked, and all the marked knowledge communities and complex knowledge network node sets are uploaded and stored in the cloud server to construct a search engine.
According to the above technical features, in step S6, the cloud server receives a search instruction input in the user-side search engine, converts text information of the search instruction into a binary code corresponding to the complex knowledge network, and transmits the binary code to the cloud server.
According to the technical characteristics, in step S7, the binary codes of the search instruction transmitted to the cloud server are compared with the complex knowledge network nodes in the cloud server one by one to obtain network nodes of the search instruction, and the binary codes of the search instruction transmitted to the cloud server are compared with the complex knowledge network nodes in the cloud server one by one again to be compared with the network nodes of the search instruction obtained for the first time, and if the binary codes are the same, the binary codes of the search instruction are output, and if the binary codes of the search instruction are different, the binary codes of the search instruction are compared again.
According to the technical characteristics, in step S8, substituting the obtained network nodes of the search instruction into the partitioned knowledge communities in the cloud server to compare one by one, so as to obtain the knowledge communities corresponding to the network nodes of the search instruction, substituting the obtained network nodes of the search instruction into the partitioned knowledge communities in the cloud server again to compare one by one, comparing the obtained network nodes with the first obtained comparison result, and if the obtained network nodes are the same, outputting the comparison result, otherwise, comparing the obtained network nodes again.
When the knowledge community discovery method based on the complex network is used, firstly, the inventory knowledge network and the knowledge networks in the Internet are selected locally to be compared one by one, repeated knowledge network contents are removed, the rest knowledge network contents are unified and sorted to obtain the complex knowledge network, then the complex knowledge network is selected, and the complex knowledge network is subjected to relative position difference of complex knowledge network nodesMarking all nodes of the network to obtain a node set of the complex knowledge network, then selecting internal nodes of the existing knowledge community, and respectively connecting the nodes of the complex knowledge network with the nodes
Substituting the algorithm into the knowledge community to calculate, dividing the existing knowledge community into a plurality of knowledge communities, and then collecting the complex knowledge network nodes to calculate
Substituting the algorithm into the knowledge community to divide the complex knowledge network nodes into corresponding knowledge communities, wherein if the node i belongs to the community r, the node i is 1, otherwise, the node i is 0, and the node i has delta (c)i，cj)＝∑SirSjrj，δ (ci，cj) The larger the value of (d), the greater the correlation of the node i with the community r;
then selecting divided knowledge communities and complex knowledge network node sets divided according to the corresponding knowledge communities, marking each knowledge community and the corresponding complex knowledge network node set, uploading and storing all the marked knowledge communities and complex knowledge network node sets in a cloud server, and constructing a search engine by the aid of the knowledge communities and the complex knowledge network node sets, wherein when a user uses the search engine, an input search instruction is transmitted to the cloud server, the cloud server converts text information of the search instruction into binary codes corresponding to the complex knowledge network, transmits the binary codes to the cloud server, repeatedly compares the binary codes of the search instruction transmitted to the cloud server with the complex knowledge network nodes in the cloud server to obtain accurate numerical values of the network nodes of the search instruction, and then substitutes the obtained network nodes of the search instruction into the divided knowledge communities in the cloud server for repeated comparison, and obtaining accurate knowledge community data corresponding to the search instruction network nodes, and finally transmitting the obtained knowledge community corresponding to the search instruction network nodes to a user terminal for output and display.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A knowledge community discovery method based on a complex network is characterized by comprising the following steps:
s1, selecting: locally selecting a stock knowledge network and a knowledge network in the Internet for comparison to obtain a complex knowledge network;
s2, label set: selecting a complex knowledge network, and marking nodes of the complex knowledge network to obtain a node set of the complex knowledge network;
s3, community division: selecting internal nodes of the existing knowledge community, substituting the complex knowledge network nodes into the knowledge community respectively, and dividing the existing knowledge community into a plurality of knowledge communities;
s4, substitution calculation: substituting the obtained complex knowledge network node set into a plurality of divided knowledge communities, and dividing the complex knowledge network nodes into corresponding knowledge communities;
s5, uploading and constructing: selecting the divided knowledge communities and the complex knowledge network node sets divided according to the corresponding knowledge communities, uploading and storing the divided knowledge communities and the complex knowledge network node sets in a cloud server, and constructing a search engine according to the divided knowledge communities and the complex knowledge network node sets;
s6, command search: receiving a search instruction input in a search engine of a user side, converting characters of the search instruction into binary codes, and transmitting the binary codes to a cloud server;
s7, comparison and transmission: comparing the binary codes of the search instruction transmitted into the cloud server with the complex knowledge network nodes in the cloud server to obtain the network nodes of the search instruction;
s8, substitution comparison: substituting the obtained network nodes of the search instruction into the divided knowledge communities in the cloud server, and comparing to obtain the knowledge communities corresponding to the network nodes of the search instruction;
s9, outputting the result: and transmitting the obtained knowledge community corresponding to the search instruction network node to a user terminal, and outputting and displaying.
2. The knowledge community discovery method based on the complex network as claimed in claim 1, wherein in step S1, the inventory knowledge network is selected from the local and compared with the knowledge networks in the internet one by one, the inventory knowledge network refers to the knowledge data stored in the local host hard disk, the repeated knowledge network contents are removed, and the remaining knowledge network contents are unified and sorted to obtain the complex knowledge network.
3. The knowledge community discovery method based on the complex network as claimed in claim 1, wherein in the step S2, the complex knowledge network is selected, and all nodes of the complex knowledge network are marked according to the difference of the relative positions of the nodes of the complex knowledge network, so as to obtain the node set of the complex knowledge network.
4. The knowledge community discovery method based on complex network as claimed in claim 1, wherein in said step S3, the algorithm of community partition substitution is
Wherein, yijIs the number of internal node pairs of the community,
is the total number of network nodes, the larger the result value of R (Y, Y'), the more reasonable the community partition.
5. The knowledge community discovery method based on complex network as claimed in claim 1, wherein in step S4, the algorithm for substituting the complex knowledge network nodes into the knowledge community is:
Sira matrix defined as n r, n being the number of nodes, r being the number of communities, where B is a modular matrix whose elements are:
the possible space of community division is searched, the community division of the maximum Q value can be obtained, and the substituted complex knowledge network nodes are divided according to the corresponding knowledge communities.
6. The method for discovering knowledge community based on complex network as claimed in claim 5, wherein in step S4, if node i belongs to community r, it is 1, otherwise it is 0, then there are:
δ(ci,cj)＝∑SirSjrj，
δ(ci,cj) Refers to the correlation, δ (c), between node i and community ri,cj) The larger the value of (d), the greater the correlation of the node i with the community r.
7. The knowledge community discovery method based on the complex network as claimed in claim 1, wherein in step S5, the divided knowledge communities and the complex knowledge network node sets divided according to the corresponding knowledge communities are selected, each knowledge community and the corresponding complex knowledge network node set are marked, and all the marked knowledge communities and the complex knowledge network node sets are uploaded and stored in a cloud server to construct a search engine.
8. The knowledge community discovery method according to claim 1, wherein in step S6, the cloud server receives a search command input in a user search engine, converts text information of the search command into a binary code corresponding to the complex knowledge network, and transmits the binary code to the cloud server.
9. The knowledge community discovery method based on the complex network as claimed in claim 1, wherein in step S7, the binary codes of the search instruction transmitted to the cloud server are compared with the complex knowledge network nodes in the cloud server one by one to obtain the network nodes of the search instruction, and the binary codes of the search instruction transmitted to the cloud server are compared with the complex knowledge network nodes in the cloud server one by one to compare with the network nodes of the search instruction obtained for the first time, if the binary codes are the same, the search instruction is output, and if the binary codes are different, the search instruction is compared again.
10. The knowledge community discovery method based on the complex network as claimed in claim 1, wherein in step S8, the obtained network nodes of the search instruction are substituted into the partitioned knowledge communities in the cloud server to perform one-by-one comparison, so as to obtain the knowledge communities corresponding to the network nodes of the search instruction, and the obtained network nodes of the search instruction are substituted into the partitioned knowledge communities in the cloud server to perform one-by-one comparison, so as to perform comparison with the first obtained comparison result, and if the obtained network nodes are the same, the comparison result is output, and if the obtained network nodes are different, the comparison is performed again.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112994933A (en) * | 2021-02-07 | 2021-06-18 | 河北师范大学 | Generalized community discovery method for complex network |
| CN112989189A (en) * | 2021-03-08 | 2021-06-18 | 武汉大学 | Structural hole node searching method based on hyperbolic geometric space |
| CN118445714A (en) * | 2024-07-08 | 2024-08-06 | 中邮消费金融有限公司 | Community detection method, device, equipment and storage medium |
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2019
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112994933A (en) * | 2021-02-07 | 2021-06-18 | 河北师范大学 | Generalized community discovery method for complex network |
| CN112994933B (en) * | 2021-02-07 | 2022-09-06 | 河北师范大学 | Generalized community discovery method for complex network |
| CN112989189A (en) * | 2021-03-08 | 2021-06-18 | 武汉大学 | Structural hole node searching method based on hyperbolic geometric space |
| CN118445714A (en) * | 2024-07-08 | 2024-08-06 | 中邮消费金融有限公司 | Community detection method, device, equipment and storage medium |
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Application publication date: 20200508 |