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Identify multiple seeds for influence maximization by statistical physics approach and multihop coverage
Applied Network Science volume 7, Article number: 52 (2022)
Abstract
Finding the influential vertexes as seeds in a real network is an important problem which relates to wide applications. However, some conventional heuristic methods do not consider the overlap phenomenon. In order to avoid the overlap of spreading, we propose a new method in combing the statistical physics approach and multihop coverage. We also propose a faster epidemic model which does not need the averaging of stochastic behavior. Through the computer simulation, the obtained results show that our method can outperforms other conventional methods in the meaning of stronger spreading power per seed.
Introduction
Influence maximum problem (IMP) is an optimization problem for finding a small subset of influential vertexes as \(N_s\) seeds which maximize the influence represented by the number of activated vertexes from the seeds in a social network, \(N_s\ge 1\) is a constant number. The problem has many applications such as the viral marketing (Valente and Davis 1999; Guo et al. 2021b), brain activation (Morone et al. 2017), information dissemination (Lü et al. 2011) of community (Guo and Wu 2020), rumor blocking (Guo et al. 2021a) and halting global epidemic outbreaks in contact networks (Xu et al. 2020). For the maximization, a diffusion model is studied to simulate information propagation from active individuals. The typical models are called Independent Cascade (IC) and Linear Threshold (LT) models (Kempe et al. 2003). Note that a special case of IC with a constant infection probability on every links is the susceptibleinfectedrecovered (SIR) model (PastorSatorras et al. 2015) as mentioned later. However, the IMP is NPhard (Karp 1972) for both IC and LT models (PastorSatorras et al. 2015). Thus, many researchers have designed heuristic methods for finding single or multiple seeds by using local or global network properties with spreading power, such as degree centrality (BorgeHolthoefer et al. 2012; Tanaka et al. 2012), kcore (Kitsak et al. 2010), local centrality (Chen et al. 2012), local structure centrality (Gao et al. 2014), and collective influence (Teng et al. 2016). However, there are various drawbacks of these heuristic methods. For example, degree centrality is a straightforward and efficient method, however it considers only the power of direct infections. When two hubs are adjacent to each other, the spreading areas overlap heavily. Although some wellknown global methods such as betweenness centrality (BC) (Freeman 1977) and closeness centrality (CC) (Sabidussi 1966) can give better results (Dey et al. 2021) for finding multiple seeds, they are unsuitable for very largescale social networks because of the high computational complexity (Guzman et al. 2014) of \(O(\mid V\mid \times \mid E\mid )\) for BC or \(O(\mid V\mid ^2)\) for CC. Where V and E denote sets of vertexes and edges, respectively. Although, various efforts have been made on the above research, the design of more effective method is still an open issue especially for finding multiple seeds.
On the other hands, for several NPhard problems, there exist practically superior approximate algorithms in statistical physics approach. This gives our motivation for considering a new method to finding multiple seeds. In application point of view, the following problem setting to avoid the overlap of spreading from a fixed number of \(N_s\) seeds for the IMP:

How to determine the the number \(N_s\) of seeds? \(\Rightarrow\) We propose an applying of the extended minimum vertex cover (VC) on lhop coverage.

Is our method is better than the conventional selections of seeds as an approximate solution for the IMP? \(\Rightarrow\) The spreading powers in our method and conventional methods are compared through numerical simulations.
Our innovative idea is a combination of lhop coverage in computer science and the statistical physics approach (Weigt and Zhou 2006) for the minimum VC in addition through a faster simulation of information spreading. Note that these two research fields are quite different and not easily contacted. Here, the lhop coverage means that seeds infect their lhop neighbors. As the special case of \(l=1\), the set cover, dominating set, and the VC problems are corresponded to 1hop coverage. Note that, minimum set cover problem can be reducted to the minimum VC problem (Karp 1972). However, the minimum VC problem is NPhard (Karp 1972). In order to efficiently estimate the set of the minimum VC with global spreading power, we focus on collective computation by local interactions through messagepassings based on statistical physics (Weigt and Zhou 2006). Moreover, to reduce the calculation time, we propose a faster simulation based on SIR model inspired from the collective influence (Teng et al. 2016) in physics community of network science. It does not need the average of behavior, therefore it is expected to be the number of samples times faster than the conventional SIR model. Because the SIR model (PastorSatorras et al. 2015) is usually applied to perform the spreading process from multiple seeds, however many trials of spreading is necessary for the averaging of stochastic behavior. When a network is very large, the conventional SIR model requires a lot of time in the averaging of stochastic behavior.
The organization of this paper is as follows. In section “Our Combination Methods”, we briefly review the statistical physics approach and propose our method. The conventional SIR model and our faster MPSIR model are introduced in the subsection “Faster MPSIR model”. Through computer simulation, the spreading power of our method and other heuristic methods are compared in the section “Simulation Results”. Conclusion are given in the last section. In section “Appendix”, we explain the conventional heuristic methods for finding seeds.
Our combination method
Although the conventional heuristic methods (BorgeHolthoefer et al. 2012; Tanaka et al. 2012; Kitsak et al. 2010; Chen et al. 2012; Gao et al. 2014; Teng et al. 2016) can be applied to find multiple seeds, they do not consider the overlap phenomena. As shown in Fig. 1a, when hubs are near to each other, High degree(HD) method (BorgeHolthoefer et al. 2012; Tanaka et al. 2012) is not suitable for finding multiple seeds. Because their spreading areas heavily overlap. In order to avoid the overlap, we consider a new method inspired from a statistical physics approach and lhop coverage.
The outline of our combination method is as follows. First, the number \(N_s\) of seeds is determined by the lhop coverage, in which each distance between seeds is more than l hops. A fixed number \(N_s\) are corresponding to l=1, 2, 3, and 4 in order to compare the power of information spreading in the next section. Second, we explain the lhop coverage. As shown in Fig. 2, vertex i is chosen as the first candidate of VC. After removing the vertex i and its lhop neighbors, as the second candidate of VC, vertex j is chosen from the remaining network. Then, repeat the above steps until no vertexes exist in the network. The symbol table for our method is shown in Table 1.
Applying a survey propagation to minimum VC
We briefly review the approximate algorithm called survey propagation for the minimum VC problem. In the algorithm (Weigt and Zhou 2006), each vertex i has one of the three states: covered (state 1), never covered (state 0), or sometimes covered and sometimes not (joker state \(*\)). Note that the joker state \(*\) is between the state 0 and 1. The number of covered states can be regulated in the extended search space by introducing joker state. That is the reason why it is called survey propagation. As shown in Fig. 3, these probabilities are denoted as \({\hat{\pi }}_{j\rightarrow i}^{(1)}\) (state 1), \({\hat{\pi }}_{j\rightarrow i}^{(0)}\) (state 0), and \({\hat{\pi }}_{j\rightarrow i}^{(*)}\) (joker state \(*\)), respectively. We take care that the following messagepassing for estimating the minimum VC differs from information spreading on SIR model (PastorSatorras et al. 2015), although the messagepassing and information spreading are similar words. For each vertex i, the messagepassing equations (Weigt and Zhou 2006) are given by
where \(\partial i\backslash j\) is the set of the nearest neighbors of vertex i but not including j, \(e^{y}\) is a penalty factor for minimizing the size of VC, y is an inverse temperature parameter. The normalization constant is given by
For each link \(i\rightarrow k\), the probability is also given by
Equations (3)–(6) are calculated through T round iterations. After the convergence, the vertex i with the largest \({\hat{\pi }}_{i}^{(1)}\) is selected as the VC. Then it is removed and recalculate the \({\hat{\pi }}_{i}\) until all vertexes are covered in the following decimation process on the lhop coverage. The detail of the extended minimum VC on lhop coverage is described as follows
 Step 1:

By using Eqs. (3)–(6), the probability \({\hat{\pi }}_{i}^{(1)}\) of vertex i is calculated for estimating the minimum VC.
 Step 2:

As the decimation process, the vertex j with the highest \({\hat{\pi }}_{j}^{(1)}\) is selected as a seed, the chosen vertex j and its \(\partial Ball(j,l1)\) are removed from the network. We emphasize that the \(\partial Ball(j,l1)\) is represented the lhop coverage. The number of seeds are updated as \(N_{s} \leftarrow N_{s}+1\) (initially set as \(N_{s}=0\)).
 Step 3:

Repeat Steps 1 and 2 until all vertexes have been removed in the network. Finally, the size of multiple seeds is obtained as \(N_{s}\).
Faster MPSIR model
Let us consider the averaging behavior in a stochastic SIR epidemic model (we call it AVGSIR) (PastorSatorras et al. 2015) with three states S: susceptible (inactive) vertexes represents the individuals susceptible to the disease, I: infected (active) vertexes denotes the individuals that have been infected and are able to spread the disease to susceptible individuals, and R: recovered stands for individuals that have been recovered and will never be infected again (PastorSatorras et al. 2015). At each time step, in the spreading process, an infected vertex changes the states of its neighbors from S to I with probability \(\beta =\lambda \frac{\langle k\rangle }{\langle k^2\rangle }\) (PastorSatorras and Vespignani 2002), and then changes its own state from I to R with recovery probability \(\mu =1\). Usually, the conventional AVGSIR model is applied to perform the spreading process from a set of vertexes as multiple seeds, however many trials of spreading is necessary for the averaging of stochastic behavior with probability \(\beta\) in samplings. It means that if the size of the network is very large, AVGSIR model requires a lot of time for the averaging. We set sample size = 1000. In order to reduce the calculation time, we consider the following messagepassing equations inspired from that in CI (Teng et al. 2016).
where \(P_{i}^{I}(t+1)\), \(P_{i}^{R}(t+1)\), and \(P_{i}^{s}(t+1)\) denote the probabilities of states I, R, and S for vertex i at time \(t+1\), respectively. Note that already averaged probability values \(P_{i}^{I}(t)\), \(P_{i}^{R}(t)\), and \(P_{i}^{s}(t)\) are updated by time step. We call it MPSIR model. These messagepassing Eqs. (7), (8), and (9) are also physics approach. As the remarkable difference, MPSIR model is based on already averaged probability variables, therefore it does not need many samples for averaging stochastic behavior.
In summary, there are two contributions to algorithm design as follows:

By combining survey propagation for finding the minimum VC and lhop coverage, we propose a new method for finding multiple seeds.

We propose the faster MPSIR model by messagepassing.
Simulation results
The minimum VC by survey propagation versus 2approximation method
We compare the survey propagation and 2approximation method through numerical simulations in a realistic network named LastFM to show that the survey propagation can efficiently estimate the minimum VC. As shown in Table 2, the solution by the survey propagation seems to be nearly optimal, while that by 2approximation method (BarYehuda and Even 1985) almost double size of the optimal solution. Note that the survey propagation is a statistical physics approach, the 2approximation method is a computer science approach with guaranteed accuracy of the size at most twice. For comparing with the survey propagation, we also apply the belief propagation algorithm (Zhou 2013) to estimate the VC from the feedback vertex set (FVS). Because the minimum FVS can be reducted to the minimum VC. However, after removing the FVS, the remaining part of network becomes trees (forest). As shown in Fig. 4, we apply the well known method (Chen and Jost 2012) to divide the tree into odd and even layer, and select one of the layers (odd or even) whose size is smaller as VC. Table 2 shows that \(\mid VC \mid\) estimated by the survey propagation is slightly better than \(\mid VC \mid\) estimated by the belief propagation. When the inverse temperature parameter is set as \(y=7\), the result of the minimum VC is the best of the minimum size. Moreover, Table 3 shows that \(T=50\) round gets the best result for the minimum VC. Therefore, we apply the survey propagation with \(y=7\) and \(T=50\) in the following part.
Faster MPSIR versus AVGSIR
In this subsection, we show that Faster MPSIR and AVGSIR models have similar spreading behaviors. In Fig. 5, we investigate the spreading power on AVGSIR and MPSIR models for three different sizes of seed 885 (\(l=2\)), 516 (\(l=3\)), and 407 (\(l=4\)) with \(\beta =0.12\) at percolation threshold \(\lambda \frac{\langle k\rangle }{\langle k^2\rangle }\), \(\lambda =1\). These values of l give different size \(N_s\) of seeds. The rate of seeds are \(N_s/N=0.12\) (\(l=2\)), 0.07 (\(l=3\)), and 0.05 (\(l=4\)), respectively. Note that \(N_s/N\le 20\%\) is realistic (Kitsak et al. 2010). Here, we define \(S(t)=\sum _{i=1}^{N}P_{i}^{S}(t)/N\), \(I(t)=\sum _{i=1}^{N}P_{i}^{I}(t)/N\), \(R(t)=1S(t)I(t)\). In Fig. 5a–c, I(t) monotonically increases, decreases, and finally converges to zero. R(t) monotonically increases and converges to 0.4. S(t) monotonically decreases and converges to 0.6. The black lines with circle, square, and triangle marks denote the probabilities of state S, I, and R on AVGSIR model. The red lines with circle, square, and triangle marks denote the probabilities of state S, I, and R on MPSIR model, respectively. Although the size of seeds is different, the red and black lines of each state S, I, and R on MPSIR and AVGSIR models are almost coincided. In addition, R(t) converges to 0.4 in (a), 0.37 in (b), and 0.35 in (c) for \(t^{*}>t_c\) (\(t_c\): it is defined at the convergent time, when all infected vertexes are recovered.). Moreover, as l increases, \(t_c\) also increases gradually (\(t_c=5\) in (a), \(t_c=7\) in (b), and \(t_c=8\) in (c)). Note that \(S(t^{*})+R(t^{*})=1\) because of \(I(t^{*})=0\). Even if the red and black lines for each state S, I, and R on MPSIR and AVGSIR models are almost coincided, the MPSIR is approximately the number of samples times faster than the AVGSIR (since the MPSIR does not need the averaging). Besides, before the convergent time \(t_c\) (early spreading), the red and black lines on MPSIR and AVGSIR are slightly different. Since there are some gap between the highest R(t) and the lowest R(t) in samples on AVGSIR model as shown in Table 4. Note that, the gap corresponds to the difference between the red line and the black line from \(t=2\) to \(t=7\) in Fig. 5. In other words, as the reason why the difference appears, I(t) and S(t) are underestimated on AVGSIR because of the lowest value. Although the gap between the lowest and the highest R(t) is not large, the number \(N\times R(t)\) of accumulated infection vertexes is large enough because of the network size \(N=7624\). Moreover, Table 5 shows our faster MPSIR model is 30 time faster than the AVGSIR, it means 1000 samples \(\approx\) \(30\times\)Trounds (\(T=50\)). The rate of speed up (calculation time of AVGSIR / MPSIR) are from 20 to 30. The detail is shown in the Additional file 1.
Our method versus conventional methods for finding seeds
We compare the spreading power from multiple seeds chosen by our method and the conventional HD, kcore, LC, LSC, and CI methods for 8 social networks. The typical result for a social network called LastFM is shown below. Note that our method and conventional methods have the same seed size. Since the CI3 outperform the other conventional methods, we consider it result as the base line. Similar results are obtained for the remaining 7 real networks in the Additional file 1.
Figure 6 shows the time evolution of accumulated infections R(t). As shown in Fig. 6a, purple line with square mark (the minimum VC is chosen as seeds) is lower than brown line with diamond mark (\(CI_2\)) and cyan line with pentagon mark (\(CI_3\)). Although the reason of lower performance is discussed later in Fig. 7, it is considered as that the minimum VC does not consider the multihop coverage and can not avoid the overlap. As shown in Fig. 6b, c, brown lines with diamond mark (\(CI_2\)), cyan lines with pentagon mark (\(CI_3\)), orange lines with inverse triangle mark (LC), and red lines with cross mark (LSC) are higher than green lines with circle mark (HD) and blue lines with triangle mark (kcore). We remark that the CI, LC, and LSC have more spreading power than the HD and kcore, because CI, LC, and LSC not only consider the nearest neighbors of seeds but also the next nearest neighbors, or nextnext nearest neighbors, and so on. Remember that, \(N_s\)=3517, 885, and 407 (\(N_s/N=\)0.46, 0.12, and 0.05). In particular, the purple lines with square mark (our method) is the highest above the green lines with circle mark (HD), blue lines with triangle mark (kcore), orange lines with inverse triangle mark (LC), red lines with cross mark (LSC), brown lines with diamond mark (\(CI_2\)), and cyan lines with pentagon mark (\(CI_3\)) on faster MPSIR model. Although the reason of higher line is discussed later in Fig. 7, it is considered as that seeds chosen by our method are located away from each other as illustrated in Fig. 1b. Moreover, after the convergent time \(t_c\), the gap between purple line with square mark and other lines in Fig. 6b is larger than ones in Fig. 6c. Because as the number of seeds becomes smaller, the spreading power per seed becomes larger. Besides, as l increases, \(t_c\) also increases. while the size of seeds decreases.
Figure 7 shows the distribution of distance \(d_{i,j}\) of each pair of seeds i,j on 2 or 4hop coverage. The peaks of two purple lines are righter than the peak of other color lines. It means that seeds chosen by our method are located more far away from each other than ones by the conventional methods. Since the larger distance of two seeds reduces the overlap, our method have more spreading power than the conventional methods. Moreover, the peak of purple line with filled square marks (at distance \(d=6\)) is righter than the peak of purple line with square marks (at distance \(d=5\)). It indicates that as l(hop) increases, the distance of seeds increases. However, there is a limitation of larger l as mentioned later with Table 6.
With different spreading rates \(\beta =\lambda \frac{\langle k\rangle }{\langle k^2\rangle }\), \(\lambda\)=2, 4, 6, and 8, we investigate the performance of our method for finding multiple seeds. Note that a higher spreading rate than the percolation threshold \(\beta =\lambda \frac{\langle k\rangle }{\langle k^2\rangle }\) (PastorSatorras and Vespignani 2002) is realistic (Moreno et al. 2002). As shown in Fig. 8, the horizontal axis indicate the infection parameter \(\lambda\) from 2 to 10 (\(\beta\) from 0.12 to 0.6). Note that the case of \(\lambda\)=2 corresponds to Fig. 6. The vertical axis \(R(t_c)\) is the accumulated infections at the convergent time \(t_c\). As shown in Fig. 8a, because of the overlap phenomena (l=1 does not consider the multihop coverage), purple lines with square mark is not the best. When \(l>1\), purple lines with square mark (our method) are always higher than others (by the conventional methods). However, we can see that the difference between our method (purple line with square mark) and others (brown lines with diamond mark, cyan lines with pentagon mark, orange lines with inverse triangle mark, and red lines with cross mark, green lines with circle mark, and blue lines with triangle mark) becomes gradually smaller as spreading rate increases with the parameter value of \(\lambda\). Because as spreading rate increases, seeds infect more vertexes.
Furthermore, from Table 6, we can see the spreading power per seed (\(N\times R(t_c)/N_s\)) chosen by our method is greater than ones by the conventional methods (each of the best performance is emphasized by bold in comparison with the methods at lvalues). In particular, as the coverage distance l increases, the spreading power per seed chosen by our method becomes larger. Thus seeds chosen by our method on the larger coverage distance l have better spreading power as l increases, although l is limited as smaller than the \(D1\) of the network. Here, D is the diameter of network defined as the maximum distance of the shortest path between vertexes. Because when l is larger than \(D1\), all vertexes are removed after the first seed are chosen. Remember that, the \(N_s\) is determined by the number of VC.
Conclusion
In summary, to efficiently find multiple seeds, we propose a new method in approximately solving the IMP problem. The key idea is a combination of the statistical physics approach for the minimum VC and lhop coverage, in order to avoid the overlap of spreading. We also propose the MPSIR model which does not need many samples for averaging stochastic behavior, therefore it is approximately number of samples / Trounds times faster than the conventional SIR model. We apply the faster MPSIR model to simulate the spreading process quickly. As obtained results for the time evolution of accumulated infections, our method can outperform other conventional methods for social networks with different sizes.
However, in multihop coverage, how many hops are optimal for avoiding overlap is still an open problem. As future work, we will consider it and give an optimal number of hop that gives the best effect for the IMP.
Moreover, there are two algorithms (Han et al. 2020; Guo et al. 2020) based on IC model. They are quite different from our method which is based on SIR model as a special case of IC model. If we consider IC model extendedly, some relations maybe exist between these two algorithms (Han et al. 2020; Guo et al. 2020) and our method for spread overlap issue. We may find some new way to solve the IMP.
Availability of data materials
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Abbreviations
 IMP:

Influence maximization problem
 IC:

Independent Cascade model
 LT:

Linear Threshold model
 SIR:

susceptibleinfectedrecovered model
 BC:

betweenness centrality
 CC:

closeness centrality
 HD:

High degree method
 LC:

Local centrality
 LSC:

Local structure centrality
 CI:

Collective influence
 FVS:

Feedback vertex set
 VC:

Vertex cover problem
 AVGSIR:

The averaging behavior in a stochastic SIR epidemic model
 MPSIR:

Message passing for SIR model
References
Bhatia NP, Szegö GP (2002) Stability theory of dynamical systems. Springer, Berlin
BorgeHolthoefer J, Rivero A, Moreno Y (2012) Locating privileged spreaders on an online social network. Phys Rev E 85(6):066123
Chen H, Jost J (2012) Minimum vertex covers and the spectrum of the normalized Laplacian on trees. Linear Algebra Appl 437(4):1089–1101
Chen D, Lü L, Shang MS, Zhang YC, Zhou T (2012) Identifying influential nodes in complex networks. Phys A Stat Mech Appl 391(4):1777–1787
Dey P, Bhattacharya S, Roy S (2021) A survey on the role of centrality as seed nodes for information propagation in large scale network. ACM/IMS Trans Data Sci 2(3):1–25
Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41
Gao S, Ma J, Chen Z, Wang G, Xing C (2014) Ranking the spreading ability of nodes in complex networks based on local structure. Phys A Stat Mech Appl 403:130–147
Guo J, Wu W (2020) Influence maximization: seeding based on community structure. ACM Trans Knowl Discov Data 14(6):1–22
Guo J, Chen T, Wu W (2021) A multifeature diffusion model: rumor blocking in social networks. IEEE/ACM Trans Netw 29(1):386–397
Guzman JD, Deckro RF, Robbins MJ, Morris JF, Ballester NA (2014) An analytical comparison of social network measures. IEEE Trans Comput Soc Syst 1(1):35–45
Han K, Xiao X, Chen W, Sun A, Tang X, Lim A, Huang K, Tang J (2020) Efficient approximation algorithms for adaptive influence maximization. Int J Very Large Data Bases 29:1385–1406
Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888–893
Lü L, Chen DB, Zhou T (2011) The small world yields the most effective information spreading. New J Phys 13(12):123005
Moreno Y, PastorSatorras R, Vespignani A (2002) Epidemic outbreaks in complex heterogeneous networks. Eur Phys J B Condens Matter Complex Syst 26(4):521–529
Morone F, Roth K, Min B, Stanley HE, Makse HA (2017) Model of brain activation predicts the neural collective influence map of the brain. Proc Natl Acad Sci 114(15):3849–3854
PastorSatorras R, Vespignani A (2002) Immunization of complex networks. Phys Rev E 65:036104
PastorSatorras R, Castellano C, Van Mieghem P, Vespignani A (2015) Epidemic processes in complex networks. Rev Mod Phys 87(3):925–979
Sabidussi G (1966) The centrality index of a graph. Psychometrika 31:581–603
Tanaka G, Morino K, Aihara K (2012) Dynamical robustness in complex networks: the crucial role of lowdegree nodes. Sci Rep 2(1):1–6
Teng X, Pei S, Morone F, Makse HA (2016) Collective influence of multiple spreaders evaluated by tracing real information flow in largescale social networks. Sci Rep 6(1):1–11
Valente T, Davis R (1999) Accelerating the diffusion of innovations using opinion leaders. Ann Am Acad Polit Soc Sci 566(1):55–67
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘smallworld’ networks. Nature 393(6684):440–442
Weigt M, Zhou H (2006) Message passing for vertex covers. Phys Rev E 74(4):046110
Xu Z, Rui X, He J, Wang Z, Hadzibeganovic T (2020) Superspreaders and superblockers based community evolution tracking in dynamic social networks. Knowl Based Syst 192:105377
Zhou HJ (2013) Spin glass approach to the feedback vertex set problem. Eur Phys J B 86(11):1–9
BarYehuda R, Even S (1985) A localratio theorem for approximating the weighted vertex cover problem. In: Analysis and design of algorithms for combinatorial problems, volume 109 of NorthHolland mathematics studies. NorthHolland, pp 27–45
Guo Q, Wang S, Wei Z, Chen M (2020) Influence maximization revisited: efficient reverse reachable set generation with bound tightened. In: Proceedings of the 2020 ACM SIGMOD international conference on management of data, pp 2167–2181
Guo J, Zhang Y, Wu W (2021) An overall evaluation on benefits of competitive influence diffusion. IEEE Trans Big Data 1
Karp RM (1972) Reducibility among combinatorial problems. In: Complexity of computer computations. Springer, pp 85–103
Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp 137–146
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YH and LF designed research. LF performed research. YH and LF contributed to develop new methods, analyze data, and wrote the paper. All authors read and approved the final manuscript.
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Supplementary Information
Additional file 1
. Supplementary tables and figures.
Appendix
Appendix
The symbol table for the conventional methods is shown in Table 7.
In considering an IMP, we explain the following widelyused heuristic methods for finding seeds, whose spreading power are compared with that by our method in the next section.
High degree
The High degree (HD) method selects k vertexes in decreasing order of degrees as the influential seeds (BorgeHolthoefer et al. 2012; Tanaka et al. 2012). It needs only the local topological properties from the connecting nearest neighbors. Therefore, it is simple and efficient for finding seeds.
kcore
In kcore method (Kitsak et al. 2010), seeds are ranked according to their \(k_{s}\) values, which are calculated through the kshell decomposition. In the kshell decomposition, vertexes are removed iteratively. Firstly, leaves with \(k_{s}=1\) are removed. This pruning is repeated until there is no leaves. The peripheral kshell with index \(k_{s}=1\) consists of a set of removed vertexes. Similarly, the next kshells with index \(k_{s} \leftarrow k_{s}+1\) are extracted, the vertexes located within the core have the highest \(k_{s}\) values. Actually, in the kshell decomposition, all vertexes are divided into shells. In comparison with the peripheral vertexes, the core vertexes tend to involve larger spreading from them. Therefore, the vertex in the core with the largest \(k_{s}\) is defined as a seed.
Local centrality and local structure centrality
The HD is simple and efficient, however it neglects the global network properties. When the neighbors of a hub are leaves, the peripheral hub has weak spreading power only for a moment. In contrast, betweenness (BC) and closeness (CC) centrality consider the global information, while their calculations are slightly complicated. Thus, Local centrality (LC) considers a tradeoff between locality and timeconsuming for the calculation (Chen et al. 2012). The LC is defined as
where \(\partial u\) denotes the set of the nearest neighbors of vertex u, \(\partial Ball(w,k)\) denotes a set of vertexes within k hops from vertex w as shown in Fig. 9. \(\mid\) \(\mid\) denotes its size. As a seed, v is selected in decreasing order of \(C_{LC}(v)\). Note that LC gives similar spreading power as good as the closeness centrality (Chen et al. 2012).
In addition, Local structure centrality (LSC) is an extension of LC (Gao et al. 2014). The LSC is defined by the linear interpolation of local clustering coefficient \(C_{w}\) (Watts and Strogatz 1998) and LC with a tunable balance parameter \(0\le \alpha \le 1\).
where \(\partial Ball(u,2)\) is a set of the next nearest neighbors of vertex u. As mentioned in Gao et al. (2014), we set \(\alpha =0.7\)
Collective influence
Collective influence (CI) aims to find the minimum set of vertexes for the IMP as follows (Teng et al. 2016). At the origin \(\{v_{i \rightarrow j}\}=\{0\}\), the stability of nonlinear messagepassing equation
is determined by the largest eigenvalue of the Jacobian matrix \(\left[ \frac{\partial v_{i\rightarrow j}}{\partial v_{k\rightarrow l}}\right]\). In other words, when the largest eigenvalue is less than 1, the spreading is stopped by removing a set of vertexes \(\{i:n_i=0\}\) as influences. Thus, by using a greedy algorithm to minimize the eigenvalue, CI is derived (Bhatia and Szegö 2002) through a power method for each vertex i,
where \({\mathcal {R}}\) is the radius of the ball. The highest \(CI_{{\mathcal {R}}}(i)\) is selected as a seed. After removing the vertex i, \(CI_{{\mathcal {R}}}(i')\) is recalculated for the remaining vertexes \(i'\in V\) in the network. It needs only local topological structure within the ball of the radius \({\mathcal {R}}\) instead of the whole network.
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Liao, F., Hayashi, Y. Identify multiple seeds for influence maximization by statistical physics approach and multihop coverage. Appl Netw Sci 7, 52 (2022). https://doi.org/10.1007/s4110902200491x
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DOI: https://doi.org/10.1007/s4110902200491x
Keywords
 Influence maximum problem
 Multiple seeds
 Vertex cover problem
 Lhop coverage
 Overlapping phenomena
 SIR model
 Statistical physics approach