CN109086341B - Hot event heat measurement method applying group intelligence - Google Patents
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Abstract
The invention discloses a hot event heat measurement method applying group intelligence, which comprises the following steps: s1, describing interaction conditions among users by using an interaction graph; s2, dividing the interactive graph into continuous time slices, and respectively calculating node degree distribution, point degree centrality and aggregation coefficients in each time slice; and S3, in each time slice, comprehensively calculating the hot event heat in the current time slice according to the node degree distribution, the node degree centrality and the aggregation coefficient in the time slice. The invention uses the interactive graph which is one of important tools for complex network research to model the interactive behavior among users, uses the measurement indexes of the complex network to describe the overall characteristics of the interactive graph, embodies each stage in the development process of the hot event, is matched with the development process of the hot event, and ensures the accuracy and the effectiveness of the final measurement result.
Description
Technical Field
The invention relates to an event heat measuring method, in particular to a hot event heat measuring method for application group intelligence used in social big data analysis, and belongs to the field of artificial intelligence and data mining.
Background
With the rapid development of the internet and informatization, social networks have gradually penetrated into the daily life of people and play an increasingly important role, becoming a platform for people to share and interact. In the social network, frequent user activities urge to generate social big data, including popular opinions, views, emotions and the like, and the social network becomes a real-world map due to the existence of the factors.
The hotspot events serve as an important part in the social big data and represent the attention points of the current public. At present, data processing can be carried out on social big data through a machine learning method, then hot events in the social big data are mined, and the development trend of the hot events is predicted, so that the method has important significance for public opinion monitoring, user interest modeling and the like. The measure of the hot event heat degree is a key index in hot event mining and hot event prediction.
In the prior art, the measurement of the hot event heat degree is generally carried out in a way of counting the keyword frequency. And counting the keywords or the burst words related to the hot spot events through a natural language processing technology, calculating the occurrence frequency of the keywords or the burst words, and sequencing the hot spot events according to the frequency value. The method is widely applied to the fields of search, event indexes, hot topics and the like. However, this method suffers from problems of ambiguity, etc. in the use process.
The user is the main body in the process of occurrence, development and disappearance of the hotspot events. More users can be attracted to participate aiming at the event with higher heat; for the events of the crowd with lower popularity, the participation of the user is lower. The interaction between the users forms group intelligence to promote the development of the hotspot events, so that the measurement of the heat degree of the hotspot events and the representation of the group intelligence show a strongly correlated trend in nature.
In summary, how to provide a brand-new event heat measurement method, which fully utilizes group intelligence to accurately and quickly complete heat measurement of a hot event, becomes a problem to be solved by technical personnel in the field at present.
Disclosure of Invention
In view of the foregoing defects in the prior art, an object of the present invention is to provide a hot event heat measurement method for applying group intelligence used in social big data analysis.
Specifically, the method comprises the following steps:
s1, describing, namely describing interaction conditions among users by using an interaction graph;
s2, a preprocessing step, namely dividing the interactive graph into continuous time slices, and respectively calculating node degree distribution, point degree centrality and an aggregation coefficient in each time slice;
and S3, a comprehensive measurement step, namely comprehensively calculating the hot event heat in the current time slice in each time slice according to the node degree distribution, the node degree centrality and the aggregation coefficient in the time slice.
Preferably, each node in the interaction graph corresponds to one user, and each edge in the interaction graph corresponds to an interaction relationship between users.
Preferably, each of the nodes has node attributes including user age, user occupation, and user location.
Preferably, the interaction relationship includes forwarding, comment and like among users, each of the edges has an edge attribute, and the edge attribute includes an interaction frequency.
Preferably, the preprocessing step S2 includes:
s21, dividing the interactive graph into m continuous time slices at preset time intervals;
s22, calculating node degree distribution D in each time slice;
s23, calculating a point-degree centrality DC in each time slice;
and S24, calculating the clustering coefficient CL in each time slice.
Preferably, in each time slice, the node degree distribution D is expressed by a power index of the node degree distribution in the time slice in a double logarithmic coordinate, the centrality of point degree DC is expressed by an average value of the centrality of node degrees in the time slice, and the clustering coefficient CL is expressed by a clustering coefficient value in the time slice.
Preferably, S3 the step of integrating metrics includes:
s31, acquiring an aggregation coefficient CL, a point degree centrality DC and a node degree distribution D in a single time slice;
s32, the acquired aggregation coefficient (x) 1 ) Node degree distribution (x) 2 ) And centroidal (x) 3 ) Carrying out non-dimensionalization treatment by the following formula,
wherein x is i Expressing node degree distribution, centrality of node degrees and cluster coefficient value, x i ' represents the result after de-dimensioning, the function max (x) being used to calculate the maximum of the three values;
s33, comparing the processed aggregation coefficients, node degree distribution and the point degree centrality in pairs to obtain a comparison matrix,
wherein the element a in the comparison matrix jk Representing the importance between two comparison values, the size of the values being proportional to the importance, where j, k = {1,2,3}.
S34, carrying out consistency check on the comparison matrix, calculating an average random consistency index CI of the comparison matrix, wherein the calculation formula is as follows,
where m denotes the matrix order, m =3, λ max (A) A maximum value representing the eigenvalues of the comparison matrix a;
s35, calculating a check index CR, wherein the calculation formula is as follows,
the value of RI is constant and changes with different orders;
s36, calculating the weight, obtaining the eigenvector after normalization processing of the eigenvector corresponding to the maximum eigenvalue of the comparison matrix, wherein the eigenvector is the weight vector of the corresponding index, the expression is,
s37, comprehensively calculating the evaluation index P of the hot event heat degree, wherein the calculation formula is as follows,
P＝0.6370*CL+0.2583*D+0.1047*DC，
where CL represents an aggregation coefficient, D represents node degree distribution, and DC represents centrality of the node degrees.
Compared with the prior art, the invention has the advantages that:
the invention uses the interactive graph which is one of important tools for complex network research to model the interactive behavior among users, uses the measurement indexes of the complex network to describe the overall characteristics of the interactive graph, embodies each stage in the development process of the hot event, is matched with the development process of the hot event, and ensures the accuracy and the effectiveness of the final measurement result.
In addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to the technical scheme of other event heat measurement methods in the field, and has strong applicability and wide application prospect.
In general, the invention gives consideration to the efficiency and the accuracy of the result in the measurement process, has good use effect and high use and popularization values.
The following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings for illustrating the embodiments of the present invention so that the technical solutions of the present invention can be understood and appreciated more easily.
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FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is an interaction graph and various index graphs of a social network over different time slices;
FIG. 3 is a schematic diagram showing the results of applying the method of the present invention;
fig. 4 is a true index of the change in heat of a hotspot event.
Detailed Description
As shown in fig. 1-2, the present invention discloses a hot event heat degree measurement method for application group intelligence used in social big data analysis.
There is a large amount of user activity in social networks. The important category is the interaction behavior of users, which is mainly caused by the interest, emotion and other aspects between users, such as forwarding, comment, like likes. Frequent interaction between users embodies group intelligence, corresponds to each stage of generation, development, climax and ending of the hot events, and keeps consistent with the development process of the hot events. Thus, the present invention measures the heat of a hotspot event using a group intelligence value.
Specifically, the hot spot event heat measurement method of the present invention includes the following steps:
s1, describing, namely describing interaction conditions among users by using an interaction graph;
s2, a preprocessing step, namely dividing the interactive graph into continuous time slices, and respectively calculating node degree distribution, point degree centrality and an aggregation coefficient in each time slice;
and S3, a comprehensive measurement step, namely comprehensively calculating the hot event heat in the current time slice in each time slice according to the node degree distribution, the node degree centrality and the aggregation coefficient in the time slice.
Each node in the interactive graph corresponds to a user, and each edge in the interactive graph corresponds to an interactive relation between the users.
Each of the nodes has node attributes including user age, user occupation, user location, and the like. The interaction relation comprises forwarding, commenting, praise and the like among users, each edge has edge attributes, and the edge attributes comprise interaction frequency.
Obviously, the interaction graph is a complex network. In the research of the complex network, the overall characteristics of the complex network can be described through indexes such as node degree distribution, node degree centrality, aggregation coefficients and the like. The node degree distribution refers to the distribution situation of each node degree in the graph and reflects the association situation of the whole network. The centrality of the node means that the node has higher centrality of the node, and the node has higher importance in the network. The clustering coefficient is a coefficient describing the degree of clustering of knots between vertices in a graph, specifically, the degree of interconnection between adjacent points of a point. Therefore, the invention provides that the interaction graph is measured by using the indexes, so that the group intelligence of user interaction and the heat of the hot event are measured.
Specifically, in the present invention, the preprocessing step S2 includes:
s21, dividing the interactive graph into m continuous time slices at preset time intervals, wherein each time slice comprises the interactive graph, a node degree distribution graph, a node degree centrality graph and a clustering coefficient graph;
s22, calculating node degree distribution D in each time slice;
s23, calculating a point-degree centrality DC in each time slice;
and S24, calculating the clustering coefficient CL in each time slice.
In each time slice, the node degree distribution D is expressed by a power exponent of the node degree distribution in the time slice in a log-log coordinate, the centrality DC of the point degree is expressed by an average value of the centrality of the node degree in the time slice, and the clustering coefficient CL is expressed by a clustering coefficient value in the time slice.
As shown in fig. 2, on a single time slice, the node degree distribution indicates that a few nodes are active (i.e., the number of nodes with a large degree is small), and most nodes are inactive and participate in one or several interactions. This situation shows that most users are free outside the hotspot event, and only a few users are the dominant objects of the event, and participate or participate in the discussion of the hotspot event to a great extent. The trend of the power exponent changes to be gradually reduced or constant with time, indicating that more and more users participate in the discussion of the hotspot event to a great extent. While the power exponent decreases at different rates over different time slices, it appears that the decreasing rate slows down over successive time slices, indicating that the user's increased level of engagement slows down.
For centrality of the degree of focus, a greater centrality of the degree of focus indicates a higher degree of user engagement in the hotspot event. As time goes on, the number of new contents about the hot event issued by the user increases, and the number of users forwarding the original issued contents also increases, so that nodes with different degrees increase (or do not change) at different increasing speeds or start to appear, that is, the participation of the users is continuously increasing.
For the clustering coefficient, the clustering coefficient is increased and then decreased along with the time, namely, in the social network, the discussion trend of the user on hot things is concentrated and then dispersed. The occurrence of a hot event is often accompanied by increased attention and discussion degree of the user; when an event becomes a hot event, more users can be attracted to pay attention, so that the heat of the hot event is increased; then when a hot event is discussed sufficiently, the discussion focus of the user is dispersed from the main stream event for discussing the hot event to the branch hot event in discussing the hot event, so that the clustering situation tends to disperse.
Therefore, in order to observe the variation trend of the above three values on a continuous time slice, for each index, an index sequence of the interaction graph is formed, that is, the node degree sequence, the point degree centrality sequence, and the clustering coefficient sequence described above.
S3, the step of comprehensive measurement comprises the following steps:
s31, acquiring an aggregation coefficient, a point degree centrality and a node degree distribution in a single time slice;
s32, aggregating the acquired nodes with a coefficient (x) 1 ) Node degree distribution (x) 2 ) And centrolities of points (x) 3 ) Carrying out non-dimensionalization treatment by the following formula,
wherein x is i Expressing node degree distribution, centrality of node degrees and cluster coefficient value, x i ' represents the result after de-dimensioning, the function max (x) being used to calculate the maximum of the three values;
s33, comparing the processed aggregation coefficients, the node degrees and the centrality of the node degrees in pairs to obtain a comparison matrix,
wherein the element a in the comparison matrix jk Indicating the importance between two contrasting values, a larger value indicating a value x i Ratio x j More importantly, itWhere, j, k = {1,2,3};
s34, carrying out consistency check on the comparison matrix, calculating an average random consistency index CI of the comparison matrix, wherein the calculation formula is as follows,
where m denotes the matrix order, m =3, λ max (A) A maximum value representing the eigenvalues of the comparison matrix a;
s35, calculating a check index CR, wherein the calculation formula is as follows,
wherein, the value of RI is constant and changes with different orders;
s36, calculating the weight, obtaining the eigenvector after normalization processing of the eigenvector corresponding to the maximum eigenvalue of the comparison matrix, wherein the eigenvector is the weight vector of the corresponding index, the expression is,
s37, comprehensively calculating an evaluation index P of the hot spot event heat degree, wherein the calculation formula is as follows,
P＝0.6370*CL+0.2583*D+0.1047*DC，
where CL represents an aggregation coefficient, D represents node degree distribution, and DC represents centrality of the node degrees.
Next, the present invention is further explained with reference to a specific embodiment, and the method of the present invention is applied to measure the heat of the hot spot event in the new wave microblog data. 228129 pieces of microblog data of about 4 months and 5 days of the hot event of 'Qingming festival' are selected. And constructing an interactive graph by taking the microblog users as nodes and taking the forwarding relation between the users as edges.
Time slices are divided by 1 hour on the constructed interactive map, and the heat of the hot spot event is calculated in each time slice by applying the method provided by the invention, and the result is shown in fig. 3. To test the effectiveness of the method of the present invention, the Baidu exponential trend for the "Qingming festival" hot event is shown in FIG. 4. Through comparison of the two graphs, the method provided by the invention is generally consistent with the development trend of real events in measuring the hot spot event heat degree, and has effectiveness.
The invention uses the interactive graph which is one of important tools for complex network research to model the interactive behavior among users, uses the measurement indexes of the complex network to describe the overall characteristics of the interactive graph, embodies each stage in the development process of the hot event, is matched with the development process of the hot event, and ensures the accuracy and the effectiveness of the final measurement result.
In addition, the invention also provides reference for other related problems in the same field, can be expanded and extended on the basis of the reference, is applied to the technical scheme of other event heat measurement methods in the field, and has strong applicability and wide application prospect.
In general, the method gives consideration to the efficiency in the measurement process and the accuracy of the result, has good use effect and high use and popularization values.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (1)
1. A hot event heat measurement method applying group intelligence is characterized by comprising the following steps:
s1, describing, namely describing interaction conditions among users by using an interaction graph;
s2, a preprocessing step, namely dividing the interactive graph into continuous time slices, and respectively calculating node degree distribution, point degree centrality and an aggregation coefficient in each time slice;
the pretreatment step comprises:
s21, dividing the interactive graph into m continuous time slices at preset time intervals;
s22, calculating node degree distribution D in each time slice;
s23, calculating a point-degree centrality DC in each time slice;
s24, calculating an aggregation coefficient CL in each time slice;
in each time slice, the node degree distribution D is expressed by using a power index of the node degree distribution in the time slice under a log-log coordinate, the node degree centrality DC is expressed by using an average value of the node degree centrality in the time slice, and the aggregation coefficient CL is expressed by using an aggregation coefficient value in the time slice;
each node in the interactive graph corresponds to a user, and each edge in the interactive graph corresponds to an interactive relation between the users;
each node has node attributes which comprise user age, user occupation and user location;
the interaction relation comprises forwarding, commenting and praise among users, each edge has an edge attribute, and the edge attribute comprises interaction frequency;
s3, a comprehensive measurement step, namely comprehensively calculating the hot event heat in the current time slice in each time slice according to the node degree distribution, the node degree centrality and the aggregation coefficient in the time slice;
the step of comprehensive measurement comprises:
s31, acquiring an aggregation coefficient CL, a point degree centrality DC and a point degree distribution D in a single time slice;
s32, the acquired clustering coefficient x 1 Node degree distribution x 2 And centrolities of points x 3 Carrying out non-dimensionalization treatment by the following formula,
wherein x is i Expressing node degree distribution, centrality of node degrees and cluster coefficient value, x i ' denotes the result after the dimensioning, function max (x) i ) For calculating the maximum of the three values;
s33, comparing the processed aggregation coefficients, node degree distribution and the point degree centrality in pairs to obtain a comparison matrix,
wherein the element a in the comparison matrix jk Represents the importance between two comparison values, the size of the value being proportional to the importance, wherein j, k = {1,2,3};
s34, carrying out consistency check on the comparison matrix, calculating the average random consistency index CI of the comparison matrix, wherein the calculation formula is as follows,
where m denotes the matrix order, m =3, λ max (A) A maximum value representing the eigenvalues of the comparison matrix a;
s35, calculating a checking index CR, wherein the calculation formula is as follows,
wherein, the value of RI is constant and changes with different orders;
s36, calculating the weight, obtaining the eigenvector after normalization processing of the eigenvector corresponding to the maximum eigenvalue of the comparison matrix, wherein the eigenvector is the weight vector of the corresponding index, the expression is as follows,
s37, comprehensively calculating an evaluation index P of the hot spot event heat degree, wherein the calculation formula is as follows,
P＝0.6370*CL+0.2583*D+0.1047*DC，
where CL represents an aggregation coefficient, D represents node degree distribution, and DC represents centrality of the node degrees.
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