SCIENCE CHINA Information Sciences, Volume 62, Issue 5: 052103(2019) https://doi.org/10.1007/s11432-018-9609-7

Cumulative activation in social networks

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  • ReceivedApr 3, 2018
  • AcceptedSep 11, 2018
  • PublishedApr 3, 2019


Most studies on influence maximization focus on one-shot propagation, i.e., the influence is propagated fromseed users only once following a probabilistic diffusion model and users' activation are determined viasingle cascade.In reality it is often the case that a user needs to be cumulatively impactedby receiving enough pieces of information propagated to herbefore she makes the final purchase decision.In this paper we model such cumulative activation as the following process: first multiple pieces of information are propagated independently in the social networkfollowing the classical independent cascade model, then the user will be activated (and adopt the product) if the cumulative pieces of information she receivedreaches her cumulative activation threshold.Two optimization problems are investigated under this framework: seed minimization with cumulative activation (SM-CA), which asks how to select a seed set with minimum size such that the number of cumulatively activenodes reaches a given requirement $\eta$;influence maximization with cumulative activation (IM-CA), which asks how to choose a seed set with fixed budget to maximize the number of cumulatively active nodes.For SM-CA problem, we design a greedy algorithm that yields a bicriteria $O(\ln~n)$-approximation when $\eta=n$, where$n$ is the number of nodes in the network.For both SM-CA problem with $\eta<n$ and IM-CA problem, we prove strong inapproximability results.Despite the hardness results, we propose two efficient heuristic algorithms for SM-CA and IM-CA respectively based on the reverse reachable set approach.Experimental results on different real-world social networks show that our algorithmssignificantly outperform baseline algorithms.


This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61433014, 61502449, 61602440), National Basic Research Program of China (973) (Grant No. 2016YFB1000201).


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  • Figure 1

    (Color online) Illustration of multiple cascades. (a) $T=0$; (b) $T=1$; (c) $T=2$; (d) $T=3$.

  • Figure 2

    Figures for understanding the model. (a) CA & IC; (b) nonsubmoduarity; (c) $\eta<n$.

  • Table 1   Datasets
    Name # Node #Edge Type AOD
    Flixster 29 K 174 K Directed 6.0
    NetPHY 37 K 348 K Undirected 18.8
    DBLP 655 K 2 M Undirected 6.1

    Algorithm 1 Estimate $f(S)$ by Monte Carlo


    Output:$\hat{f}(S)$: the estimation of $f(S)$;


    $\hat{P}_u(S)=0$; $t_u=0$ for all $u~\in~V$;

    for $i=1$ to $R$

    Simulate IC diffusion from seed set $S$;

    if $u$ is activated then


    end if

    end for

    for $u\in~U$


    if $\hat{P}_u(S)\geq\tau_u$ then




    end if

    end for

    return $\hat{f}(S)$.


    Algorithm 2 Greedy algorithm for SM-CA with $\eta=n$

    Require:$G=(V,~E),~\{p_{uv}\}_{(u,~v)\in~E},\{\tau_u\}_{u\in~V},~U$, $\varepsilon$;

    Output:Seed set $S$

    $S=\emptyset$, $\hat{f}(S)=0$;

    while $\hat{f}(S)<\sum_{u\in~V}\tau_u-\varepsilon$ do

    Choose $v=\arg\max_{u\in~V\setminus~S}~[\hat{f}(S\cup~\{u\})-\hat{f}(S)]$;


    end while

    return $S$.

  • Table 2   Running time ($\tau=0.3$, $k=500$) (s)
    sf TIM$^+$ ADG-IM-CA BTG-IM-CA
    Flixster39 87 138
    NetPHY54 112 142
    DBLP 509 8865 8685

    Algorithm 3 Framework of greedy algorithm for IM-CA problem


    Output:Seed set $S$;

    Set $S=\emptyset$;

    Generate $\theta$ RR sets for each node $u\in~V$: $\{\mathcal~R_u\}_{u\in~V}$;

    Set ${\rm~req}(u)=\tau_u\theta~$ for each node $u\in~V$;

    for $j=1$ to $k$

    $x=$ SS($G,~\{p_{uv}\}_{(u,v)\in~E},~\{{\rm~req}(u)\}_{u\in~V},~~\{\mathcal~R_u\}_{u\in~V}$);


    Remove all RR Sets containing $x$;

    for each $u$ in $V$

    ${\rm~rem}(u)$: the number of RR Sets removed from $\mathcal~R_u$;


    end for

    end for

    return $S$.

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