CN109658279A - Social network relationships recommended method based on cohesion and credit worthiness - Google Patents
Social network relationships recommended method based on cohesion and credit worthiness Download PDFInfo
- Publication number
- CN109658279A CN109658279A CN201811566313.8A CN201811566313A CN109658279A CN 109658279 A CN109658279 A CN 109658279A CN 201811566313 A CN201811566313 A CN 201811566313A CN 109658279 A CN109658279 A CN 109658279A
- Authority
- CN
- China
- Prior art keywords
- user
- good friend
- dimension
- cohesion
- credit worthiness
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000005303 weighing Methods 0.000 claims 1
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 210000001367 artery Anatomy 0.000 description 1
- 238000012550 audit Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 230000002996 emotional effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 210000003462 vein Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The social network relationships recommended method based on cohesion and credit worthiness that the present invention provides a kind of, belongs to field of artificial intelligence.The present invention is by defining cohesion and credit worthiness in social network relationships, and it analyzes accordingly, calculate person to person's degree of a relation amount, the intimate degree of judgement relationship, and new social networks are established for user's intelligent recommendation accordingly, calculating process is relatively easy, and intelligent recommendation result is more accurate, the user experience is improved.
Description
Technical field
It is the invention belongs to field of artificial intelligence, in particular to a kind of to be closed based on the social networks of cohesion and credit worthiness
It is recommended method.
Background technique
With the rapid development of Internet technology, the work and life of people and the intersection of internet increasingly come closely,
Social networks has become a part indispensable for people's lives.Social networks, can be into while safeguarding user's personal relationship
One step expands new relation.Interpersonal emotional relationship in grafting society is gone with virtual network, it can be in social network
More new relations are expanded on network, the people's lives mode that it changes.How customer relationship is effectively expanded, maintenance individual
Between relationship, be the key problem that each social network-i i-platform (SNS) (Social Network Service) must solve.
Social networks is made friends on network for user and is provided convenience, but with the development of internet, number of users is got over
Come bigger, how for user to find suitable friend-making object, be the major issue that social networking service needs to solve.For user's intelligence
Energy commending friends simultaneously establish new social networks, are that the development of each social network-i i-platform has to solve the problems, such as.Existing skill
In art, for user's commending friends and the method for establishing new social networks, calculating process is complex, and the result of intelligent recommendation
Less precisely, user experience is poor.
Summary of the invention
It is an object of the invention to solve the problems, such as that good friend's intelligent recommendation exists in the prior art, propose a kind of from intimate
Degree and credit worthiness index are set out, and by a series of calculating and operating procedure, are recommended social networks automatically for user, are expanded user
Good friend space method.
A kind of social network relationships recommended method based on cohesion and credit worthiness, comprising the following steps:
S1, the direct good friend set for obtaining user A, wherein the direct good friend is the user that dimension is 1, the dimension
Add one equal to go-between's quantity between user；
S2, the relationship weight for calculating each good friend and user A in the direct good friend set of user A obtain closing with user A
It is the good friend U of maximum weightk, enable good friend UkFor target good friend；
S3, the direct good friend for obtaining the target good friend gather, good friend and use in good friend's set of the target good friend
The dimension of family A adds one relative to the dimension of the target good friend and user A, calculates each in good friend's set of the target good friend
The relationship weight of a good friend and user A, and the recommendation of user A is added well in the good friend by relationship weight not less than preset weights threshold value
Friend's set；
S4, judge whether the dimension of good friend and user A that the commending friends set is added are less than default dimension threshold value, when
When less than the default dimension threshold value, enabling the good friend that the commending friends set is added is target good friend, and process returns to the step
Rapid S3；
S5, when the dimension of good friend and user A that the commending friends set is added are not less than default dimension threshold value, obtain
The commending friends set of user A.
Further, the step S1 includes following below scheme:
Obtain direct good friend's set F that the dimension of user A is 1A={ U1,U2,...,Um, wherein the dimension is 1 table
It expresses friendship between friend without go-between.
Further, the step S2 includes following below scheme:
The commending friends collection of initialising subscriber A is combined into sky, i.e. FRA={ }, and
To the good friend U of each user Ai∈FA, calculate UiWith the relationship weight of user A
Wherein, ρ is indicated?Middle proportion, value range are [0, l]；I indicates the cohesion between user,
Its value range is [0,1],Indicate user A and UiBetween cohesion；R indicates the maximum letter in the direct good friend of user
Reputation degree, definitionC indicates that the credit worthiness of user, C value range are [0, l], i.e.,Indicate Ui
Direct good friend in maximum credit worthiness；
It obtainsMaximum valueThat is the direct good friend U of user AkWith maximumEnable UkFor target good friend.
Further, the step S3 includes following below scheme:
Obtain direct good friend's set F={ V of the target good friend1,V2,...,Vn, calculate the straight of each target good friend
Connect friendly ViWith the relationship weight of user A
Wherein,Indicate user A and ViBetween dimension, value be equal to user A and ViBetween go-between add one；σ table
Show user to the attention rate of internuncial quantity, value range is [0, l]；
To eachVi, the commending friends set FR of user A will be addedA, wherein M is preset weights threshold
Value.
Further, the step S4 includes following below scheme:
FR is added for eachAVi, judgementWhether default dimension threshold value is less than, as addition FRAViDimensionWhen less than default dimension threshold value, by ViIt is set as the target good friend, process returns to the step S3.
Beneficial effects of the present invention: recommended the present invention provides a kind of based on the social network relationships of cohesion and credit worthiness
Method, the present invention analyze accordingly by defining cohesion and credit worthiness in social network relationships, calculate person to person's relationship
Measurement, judge the intimate degree of relationship, and establish new social networks accordingly for user's intelligent recommendation, calculating process is relatively simple
It is single, and intelligent recommendation result is more accurate, the user experience is improved.
Detailed description of the invention
Fig. 1 is the flow chart of the embodiment of the present invention.
Specific embodiment
The following are some technical principles of the present invention:
1. relational links
In social networks, interpersonal relationship be in a manner of link existing for.Including " weak link " and " strong chain
Connect " two kinds of forms.Wherein what weak link embodied is the process of information flow, is that inter-trade cross-cutting information is propagated, " strong chain
Connect " then reflect everyone the most intimate relationship at one's side.SNS, can be by the money of interpersonal relationship by way of internet
Source is excavated completely,
Here it had both contained " strong link " that those are got close to and has also contained those " weak links " for not meeting long.By SNS,
User can easily recognize " friends of friends ", to find the people of oneself needs by the people recognized, extend the people of oneself
Arteries and veins.At the same time, user can also scientifically manage the interpersonal relationships net resource of oneself by this platform of SNS, win for oneself
Obtain more chances.The value root of SNS is that the authenticity of this platform information, and user provides the actual data of oneself,
Entire society's network is based entirely on realistic individual object and relationship, to provide a true, credible, effective social stage.Such as
Where valuable application is developed on this virtual social stage, effectively to promote the emotion and information friendship between friend
Stream is the key that play SNS value.
2. Six Degrees are theoretical
" Six Degrees " theoretical also referred to as six degree of separations (Six Degrees of Separation) are theoretical.This is theoretical
It can generically illustrate are as follows: " people that you are spaced between any one stranger does not exceed six, that is to say, that at most
By six people, you can recognize any one stranger." theory results from the 1960s, by American Psychologist
Mir's Glenn proposes.
The theory thinks, people by six layers of interpersonal relationships can find it is tellurian anyone.Although it is extremely
The present still only rests on controversial " hypothesis " stage, but causes the research and concern of every field scholar.
3. cohesion
New density is the far and near close and distant degree of relationship between description user and good friend, and new density is higher, illustrates user to good
Friendly relationship is closer, then from the good friend obtain information reliability it is bigger, meanwhile, the webpage of friend recommendation, the article delivered,
It is also bigger that the chance adopted is concerned to the answer of problem.Therefore the high people of those new density, it should be in relationship recommendation
More forward position.
4. credit worthiness
Credit worthiness is the degree for indicating a people and being known by the public, understanding, and the breadth and depth of social influence is evaluation name
The objective scale of gas size.The credit worthiness of one people and his personal story have a close relationship, such as one in computer
Network field had more than ten years experience and was responsible for the credit worthiness of the expert of multinomial catenet architecture design and can be significantly greater than one
The people that this field there is not experience is related at the beginning of a.The credit worthiness of one people is higher, and the webpage of his recommendation, is answered the blog delivered
The authority of problem is higher, it should be in higher priority in relationship recommendation.
In the present invention, in order to be embodied as the purpose that good friend requestor A finds suitable user B, it is desirable that A and B meets following
Three conditions:
So that a possibility that relationship of A and B is intimate as far as possible, then A obtains useful information from B is bigger, we are used here
Cohesion measures the intimate degree between A and B.
The credit worthiness of target person B should be as high as possible, then the credibility for the information that A is obtained from B is higher.
The go-between undergone from A to B should lack as far as possible, because not passing through a sponsor, cohesion will decay once,
B helps the wish of A that can also be gradually reduced.
During realizing this purpose, invention defines following variables:
Cohesion I, for describing the cohesion between user, value range is [0, l].IABIndicate that user A's and B is intimate
Degree, the more high then user A of value and B cohesion are higher, on the contrary then cohesion is lower.If if A and B did not recognized completely, IAB
=0, IAB=1 sets up when A=B.In addition, IABIt indicates from user A, i.e., the case where A recognizes B, due to can
There can be an A understanding B and B the case where not recognizing A, so IAB≠IBA.When original state, IABIt can be set by user A, it is later automatic
According between A, B activity and operation be updated.
The dimension D of cohesion, for measuring the index of cohesion transmitting.It is if A and B are direct good friends, i.e., straight therebetween
Connect understanding and without go-between, then DAB=1；If the relationship of A and B is transmitted by go-between C, i.e. A is the good friend of C, and C is B
Good friend, there are go-between C between user A and B, then DAB=2.To sum up, the dimension between two users is equal between two users
Go-between's quantity adds one.The good friend that dimension is 1 is direct good friend.
Credit worthiness C, for describing certain user in the credit worthiness of specific region, value range is [0, l].The value is closer
O indicates that the credit worthiness of user is lower, on the contrary then user's credit worthiness is higher.CAInitial value by system according to the nature of the user A
Attribute is set by backstage manager or audit crew, is updated later by system according to the activity and operation of user.
R indicates the maximum credit worthiness in the direct good friend of certain user.If the direct good friend collection of user A is combined into FA={ U1,
U2,...,Um, then the maximum credit worthiness in the direct good friend of user A
The embodiment of the present invention is described further with reference to the accompanying drawing.
Referring to Fig. 1, a kind of social network relationships recommended method based on cohesion and credit worthiness proposed by the present invention, leads to
Cross following steps realization:
S1, the direct good friend set for obtaining user A, wherein direct good friend is the user that dimension is 1, and dimension is equal to user
Between go-between's quantity add one.
In the present embodiment, direct good friend's set F that the dimension of user A is 1 is obtainedA={ U1,U2,...,Um}。
S2, the relationship weight for calculating each good friend and user A in the direct good friend set of user A obtain closing with user A
It is the good friend U of maximum weightk, enable good friend UkFor target good friend.
In the present embodiment, the commending friends collection of initialising subscriber A is combined into sky, i.e. FRA={ }, andFinally
It is deposited in the set for the user A good friend recommended.
To the direct good friend U of each user Ai∈FA, calculate UiWith the relationship weight of user A
Wherein,Represent UiIt is recommended to the degree of user A, the bigger the value the more worth recommended；ρ is indicated?Middle proportion, value range are [0, l], and the value of ρ is adjustableInWithAccounting；I is indicated between user
Cohesion, value range be [0,1],Indicate user A and UiBetween cohesion；R is indicated in the direct good friend of user
Maximum credit worthiness,C indicates that the credit worthiness of user, C value range are [0, l], i.e.,
Indicate UiDirect good friend in maximum credit worthiness；
By calculating the direct good friend of each user A and the relationship weight of user A, enabledObtain maximum valueThat is the direct good friend U of user AkWith maximumEnable UkFor target good friend.
S3, the direct good friend for obtaining target good friend gather, the dimension of good friend and user A in good friend's set of target good friend
Dimension relative to target good friend and user A adds one, calculates the pass of each good friend and user A in good friend's set of target good friend
It is weight, and relationship weight is added to the commending friends set of user A not less than the good friend of preset weights threshold value.
In the present embodiment, direct good friend's set F={ V of target good friend is obtained1,V2,...,Vn, calculate each target
The direct good friend V of good friendiWith the relationship weight of user A
Wherein,Indicate user A and ViBetween dimension；σ indicates that user to the attention rate of internuncial quantity, takes
Being worth range is [0, l], and the value is bigger, then weightInfluenced by dimension it is bigger, because go-between increase, dimension increase due to lead
The weight rate of decay is caused to become faster.
To eachVi, the commending friends set FR of user A will be addedA, wherein M is preset weights threshold
Value.
S4, judge whether the good friend that commending friends set is added and the dimension of user A are less than default dimension threshold value, when being less than
When default dimension threshold value, enabling the good friend that commending friends set is added is target good friend, and process returns to step S3.
In the present embodiment, step S4 is realized by following below scheme:
S41, the new addition commending friends set FR of judgementAViDimension whether be less than default dimension threshold value.
If S42, the V being newly addediDimension DAViLess than default dimension threshold value, then the V being newly addediIt is good to be set as target
Friend, process return to S3.
In the present embodiment, presetting dimension threshold value according to Six Degrees theory value is 6, or other values.
In the present embodiment, the V of commending friends set is added in firstiDimension is 2, in the case where dimension threshold value is equal to 6,
2 < 6, the then V being newly addediStep S3 is returned to as target good friend and continues friend recommendation, is repeated always, is added until newly
ViDimension be equal to 6, repetition terminate.
If S43, the V being newly addediDimensionNot less than default dimension threshold value, process enters step S5.
S5, when the dimension of good friend and user A that commending friends set is added are not less than default dimension threshold value, obtain user
The commending friends set of A.
In the present embodiment, after repetition in step s 4, the commending friends set FR of complete user A is obtainedA。
Those of ordinary skill in the art will understand that embodiment here be to help reader understand it is of the invention
Principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.This field it is common
Technical staff disclosed the technical disclosures can make the various various other tools for not departing from essence of the invention according to the present invention
Body variations and combinations, these variations and combinations are still within the scope of the present invention.
Claims (5)
1. a kind of social network relationships recommended method based on cohesion and credit worthiness, which comprises the following steps:
S1, the direct good friend set for obtaining user A, wherein the direct good friend is the user that dimension is 1, and the dimension is equal to
Go-between's quantity adds one between user；
S2, the relationship weight for calculating each good friend and user A in the direct good friend set of user A obtain weighing with user's A relationship
It is worth maximum good friend Uk, enable good friend UkFor target good friend；
S3, the direct good friend for obtaining the target good friend gather, the good friend's and user A in good friend's set of the target good friend
Dimension adds one relative to the dimension of the target good friend and user A, and it is good to calculate each in good friend's set of the target good friend
The relationship weight of friend and user A, and the commending friends collection by relationship weight not less than good friend's addition user A of preset weights threshold value
It closes；
S4, judge whether the dimension of good friend and user A that the commending friends set is added are less than default dimension threshold value, when being less than
When the default dimension threshold value, enabling the good friend that the commending friends set is added is target good friend, and process returns to the step S3；
S5, when the dimension of good friend and user A that the commending friends set is added are not less than default dimension threshold value, obtain user
The commending friends set of A.
2. the social network relationships recommended method based on cohesion and credit worthiness as described in claim 1, which is characterized in that institute
Stating step S1 includes following below scheme:
Obtain direct good friend's set F that the dimension of user A is 1A={ U1,U2,...,Um, wherein the dimension is 1 expression good friend
Between without go-between.
3. the social network relationships recommended method based on cohesion and credit worthiness as claimed in claim 2, which is characterized in that institute
Stating step S2 includes following below scheme:
The commending friends collection of initialising subscriber A is combined into sky, i.e. FRA={ }, and
To the good friend U of each user Ai∈FA, calculate UiWith the relationship weight of user A
Wherein, ρ is indicated?Middle proportion, value range are [0, l]；I indicates the cohesion between user, takes
Being worth range is [0,1],Indicate user A and UiBetween cohesion；R indicates the maximum credit worthiness in the direct good friend of user,
DefinitionC indicates that the credit worthiness of user, C value range are [0, l], i.e.,Indicate UiIt is straight
Connect the maximum credit worthiness in friend；
It obtainsMaximum valueThat is the direct good friend U of user AkWith maximumEnable UkFor target good friend.
4. the social network relationships recommended method based on cohesion and credit worthiness as claimed in claim 3, which is characterized in that institute
Stating step S3 includes following below scheme:
Obtain direct good friend's set F={ V of the target good friend1,V2,...,Vn, calculate the direct good of each target good friend
Friendly ViWith the relationship weight of user A
Wherein,Indicate user A and ViBetween dimension, value be equal to user A and ViBetween go-between add one；σ indicates to use
To the attention rate of internuncial quantity, value range is [0, l] at family；
To eachVi, the commending friends set FR of user A will be addedA, wherein M is preset weights threshold value.
5. the social network relationships recommended method based on cohesion and credit worthiness as claimed in claim 4, which is characterized in that institute
Stating step S4 includes following below scheme:
FR is added for eachAVi, judgementWhether default dimension threshold value is less than, as addition FRAViDimensionIt is small
When default dimension threshold value, by ViIt is set as the target good friend, process returns to the step S3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811566313.8A CN109658279A (en) | 2018-12-19 | 2018-12-19 | Social network relationships recommended method based on cohesion and credit worthiness |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811566313.8A CN109658279A (en) | 2018-12-19 | 2018-12-19 | Social network relationships recommended method based on cohesion and credit worthiness |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109658279A true CN109658279A (en) | 2019-04-19 |
Family
ID=66115437
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811566313.8A Pending CN109658279A (en) | 2018-12-19 | 2018-12-19 | Social network relationships recommended method based on cohesion and credit worthiness |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109658279A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111858692A (en) * | 2020-07-30 | 2020-10-30 | 重庆新申言科技有限公司 | System and method for calculating interpersonal relationship based on classmate records |
| CN115865485A (en) * | 2022-11-30 | 2023-03-28 | 上海纽盾科技股份有限公司 | Stranger safety precaution method and system based on meta universe |
| CN115865485B (en) * | 2022-11-30 | 2024-06-07 | 上海纽盾科技股份有限公司 | Stranger safety precaution method and system based on meta universe |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130166574A1 (en) * | 2011-12-27 | 2013-06-27 | Nhn Corporation | Social network service system and method for recommending friend of friend based on intimacy between users |
| CN104202319A (en) * | 2014-08-28 | 2014-12-10 | 北京淘友天下科技发展有限公司 | Method and device for social relation recommendation |
| CN105141499A (en) * | 2015-07-03 | 2015-12-09 | 电子科技大学 | Social network relationship recommendation method based on privacy degree and publicity degree |
-
2018
- 2018-12-19 CN CN201811566313.8A patent/CN109658279A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130166574A1 (en) * | 2011-12-27 | 2013-06-27 | Nhn Corporation | Social network service system and method for recommending friend of friend based on intimacy between users |
| CN104202319A (en) * | 2014-08-28 | 2014-12-10 | 北京淘友天下科技发展有限公司 | Method and device for social relation recommendation |
| CN105141499A (en) * | 2015-07-03 | 2015-12-09 | 电子科技大学 | Social network relationship recommendation method based on privacy degree and publicity degree |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111858692A (en) * | 2020-07-30 | 2020-10-30 | 重庆新申言科技有限公司 | System and method for calculating interpersonal relationship based on classmate records |
| CN115865485A (en) * | 2022-11-30 | 2023-03-28 | 上海纽盾科技股份有限公司 | Stranger safety precaution method and system based on meta universe |
| CN115865485B (en) * | 2022-11-30 | 2024-06-07 | 上海纽盾科技股份有限公司 | Stranger safety precaution method and system based on meta universe |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107368752B (en) | A kind of depth difference method for secret protection based on production confrontation network | |
| Pyrooz et al. | Taking stock of the relationship between gang membership and offending: A meta-analysis | |
| CN103793503B (en) | Opinion mining and classification method based on web texts | |
| CN103927398B (en) | The microblogging excavated based on maximum frequent itemsets propagandizes colony's discovery method | |
| CN109829089A (en) | Social network user method for detecting abnormality and system based on association map | |
| CN110457404A (en) | Social media account-classification method based on complex heterogeneous network | |
| CN103778186A (en) | Method for detecting sockpuppet | |
| CN105141499B (en) | It is a kind of to recommend method based on the social network relationships of secret degree and known degree | |
| CN112765653B (en) | Multi-source data fusion privacy protection method based on multi-privacy policy combination optimization | |
| CN106203935B (en) | Technical capability evaluation and Postmatch method based on user-generated content and customer relationship | |
| CN111966786A (en) | Microblog rumor detection method | |
| CN103631862B (en) | Event characteristic evolution excavation method and system based on microblogs | |
| CN107590558A (en) | A kind of microblogging forwarding Forecasting Methodology based on multilayer integrated study | |
| CN109960755B (en) | User privacy protection method based on dynamic iteration fast gradient | |
| CN105760649A (en) | Big-data-oriented creditability measuring method | |
| CN105843829A (en) | Big data credibility measurement method based on layering model | |
| CN106971107B (en) | Safety grading method for data transaction | |
| CN103457799A (en) | Microblog zombie user detection method based on relational graph | |
| CN109658279A (en) | Social network relationships recommended method based on cohesion and credit worthiness | |
| CN105205075B (en) | From the name entity sets extended method of extension and recommended method is inquired based on collaboration | |
| CN115865708B (en) | Complex social network information handling method based on SIR-D model | |
| Lossio Ventura et al. | Conversations reconstruction in the social web | |
| CN115495671A (en) | Cross-domain rumor propagation control method based on graph structure migration | |
| CN105761152A (en) | Topic participation prediction method based on triadic group in social network | |
| CN110825972B (en) | Hot topic key user discovery method based on field differentiation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190419 |