[0092] Example 2
[0093] Optimizing on the basis of Example 1, such as figure 2 As shown, step S1 can be further refined into the following steps:
[0094] S11. For a given dynamic social network, starting from the first time slice, obtain the neighbor relationship between each node in each time slice network;
[0095] S12. According to the neighbor relationship above, divide the community structure corresponding to each time slice network through the QCA algorithm.
[0096] The QCA algorithm is a fast adaptive dynamic community discovery algorithm based on modularity. It formulates different community structure update strategies for each network change, including node addition, node deletion, edge addition, and edge deletion. By maximizing modularity To determine the community affiliation of incremental nodes.
[0097] Preferably, as image 3 As shown, step S2 can be further refined into the following steps:
[0098] S21. Obtain the superspreader set of each time slice network through the Degree Discount algorithm;
[0099] S22. Obtain the superblocker set of each community corresponding to each time slice network through the CoreHD algorithm.
[0100] Specifically, the calculation method of the superspreader is
[0101] dd v = d v -2t v -(d v -t v )t v p
[0102] where dd v is the influence score of superspreader node v, d v is the degree value of superspreader node v, t v is the number of neighbor nodes of superspreader node v selected as seed nodes, and p is the propagation probability of superspreader node information in the social network.
[0103] The Degree Discount algorithm recursively uses the above formula to calculate the influence score of the node, and each time finds out the node with the largest influence score and joins the seed node set. The set of seed nodes is the above-mentioned superspreader set.
[0104] The calculation method of the superblocker is: first delete as few nodes as possible through the CoreHD algorithm, so that there is no cycle in the network after the node is deleted. Then, the node with the highest degree value is adaptively removed from the network based on 2-core decomposition to find the smallest possible set of nodes, namely the superblocker set, so that after removing these nodes, the network is decomposed into several independent connected components.
[0105] Preferably, as Figure 4 As shown, step S3 can be further refined into the following steps:
[0106] S31. Judging the type of evolution event 1 according to the calculation model of the generated event. If the superspreader node of the current time slice t does not exist in the previous time slice t-1 or is not a node in the superspreader set, then it is determined that the evolution event 1 is an event. The community represented by the superspreader node is the newly generated community in the current time slice t.
[0107] S32. Determine the type of evolution event 1 according to the calculation model of the merged event. If the two superspreader nodes in the same community in the current time slice t belong to different communities in the previous time slice t-1, then determine the evolution event 1 as In a merge event, the communities represented by the two superspreader nodes are merged in the current time slice t.
[0108] S33. Determine the type of evolution event 1 according to the calculation model of the expansion event. If the size of the superspreader node in a community in the current time slice t is larger than the size of the superspreader node in the community corresponding to the previous time slice t-1, it is determined that the evolution event 1 is expansion event, the community expands in the current time slice t.
[0109] Preferably, as Figure 5 As shown, step S4 can be further refined to include the following steps:
[0110] S41. Determine the type of evolution event 2 according to the calculation model of the disappearance event. If the superblocker node in the previous time slice t-1 does not exist in the current time slice t or is no longer a node in the superblocker set, then it is determined that the evolution event 2 is disappearance Event, the community once represented by the superblocker node dies in the current time slice t.
[0111] S42. Judging the type of evolution event 2 according to the calculation model of the split event, if the two superblocker nodes in the same community in the previous time slice t-1 are in two different communities in the current time slice t, then determine the evolution event 2 For a split event, the community in the previous time slice t-1 splits in the current time slice t.
[0112] S43. Determine the type of evolution event 2 according to the calculation model of the reduction event. If the size of the superblocker node in a certain community in the previous time slice t-1 is larger than the size of the superblocker node in the community corresponding to the current time slice t, then the evolution event 2 is determined to be reduction event, the community shrinks in the current time slice t.
[0113] Preferably, the calculation model for generating events is
[0114]
[0115]
[0116] In the formula, ss represents a superspreader node, represents the kth community at time t, SS t Represents the superspreader collection of dynamic social networks at time t, SS t-1 Represents the superspreader set of the dynamic social network at time t-1, and Birth()=1 means that the event is true.
[0117] At time t, for any The superspreader node ss is not a superspreader at time t-1, then the community to which the node ss belongs It is a newly generated community.
[0118] The calculation model of the merge event is
[0119]
[0120]
[0121] In the formula, ss 1 、ss 2 Indicates two superspreader nodes, are two communities at time t-1, is the kth community at time t, SS t-1 Represents the superspreader set of the dynamic social network at time t-1, and Merging()=1 indicates that the merge event is true.
[0122] At time t-1, there exists ss 1 、ss 2 two communities within the superspreader, but at time t, ss 1 、ss 2 in the same community within, the community A merge has occurred.
[0123] The calculation model of the expansion event is
[0124]
[0125]
[0126] In the formula, ss represents a superspreader node, is the kth community at time t-1, is the sth community at time t, SS tis the superspreader collection of dynamic social networks at time t, SS t-1 Represents the superspreader set of the dynamic social network at time t-1, and Expansion()=1 means that the expansion event is true.
[0127] At time t-1, any ss is a community The superspreader inside is the community at time t within the superspreader, and The number of superspreaders within is less than within the superspreader number, the community Expansion occurred.
[0128] Preferably, the calculation model of the disappearance event is
[0129]
[0130]
[0131] In the formula, sb represents a superblocker node, Indicates the kth community at time t-1, SB t-1 Indicates the superblocker set of the dynamic social network at time t-1, and Death()=1 indicates that the disappearance event is true.
[0132] At time t-1, any community The superblocker node sb within is no longer a superblocker at time t, then the community where sb is located Dying happened.
[0133] The calculation model of the splitting event is
[0134]
[0135]
[0136] In the formula, sb 1 、sb 2 Indicates two superblocker nodes, is the kth community at time t-1, and are two communities at time t, SB t is the superblocker set of the dynamic social network at time t, SB t-1 is the superblocker set of the dynamic social network at time t, and Splitting()=1 means that the splitting event is true.
[0137] At time t-1, the same community Two superblockersb inside 1 、sb 2 , at time t, in different communities and , then sb 1 、sb 2 the community There was a split.
[0138] The calculation model of the reduction event is
[0139]
[0140]
[0141] In the formula, sb represents a superblocker node, is the kth community at time t-1, is the sth community at time t, SB t is the superblocker set at time t, SB t-1 is the superblocker set of the dynamic social network at time t-1, and Reduction()=1 means that the reduction event is true.
[0142] At time t-1, the community Any supersblcokersb within the community at time t within the superblocker, but the community There are more superblockers in the community than in the community The number of superblockers within the community A shrinkage has occurred.
[0143] Compared with embodiment 1, this embodiment further restricts steps S1 to S4, making full use of the characteristics of superspreader node's strong dissemination to judge generation, merger and expansion events, so that the judgment of evolution event 1 type is more accurate and fully The disappearance, splitting and shrinking events are judged by using the superblocker node's strong destructive characteristics to the social network connectivity, which makes the judgment of the evolution event 2 type more accurate.
