1,537 research outputs found
Method maximizing the spread of influence in directed signed weighted graphs
We propose a new method for maximizing the spread of influence, based on the identification of significant factors of the total energy of a control system. The model of a socio-economic system can be represented in the form of cognitive maps that are directed signed weighted graphs with cause-and-effect relationships and cycles. Identification and selection of target factors and effective control factors of a system is carried out as a solution to the optimal control problem. The influences are determined by the solution to optimization problem of maximizing the objective function, leading to matrix symmetrization. The gear-ratio symmetrization is based on computing the similarity extent of fan-beam structures of the influence spread of vertices v_i and v_j to all other vertices. This approach provides the real computational domain and correctness of solving the optimal control problem. In addition, it does not impose requirements for graphs to be ordering relationships, to have a matrix of special type or to fulfill stability conditions. In this paper, determination of new metrics of vertices, indicating and estimating the extent and the ability to effectively control, are likewise offered. Additionally, we provide experimental results over real cognitive models in support
Beyond Worst-Case (In)approximability of Nonsubmodular Influence Maximization
We consider the problem of maximizing the spread of influence in a social
network by choosing a fixed number of initial seeds, formally referred to as
the influence maximization problem. It admits a -factor approximation
algorithm if the influence function is submodular. Otherwise, in the worst
case, the problem is NP-hard to approximate to within a factor of
. This paper studies whether this worst-case hardness result
can be circumvented by making assumptions about either the underlying network
topology or the cascade model. All of our assumptions are motivated by many
real life social network cascades.
First, we present strong inapproximability results for a very restricted
class of networks called the (stochastic) hierarchical blockmodel, a special
case of the well-studied (stochastic) blockmodel in which relationships between
blocks admit a tree structure. We also provide a dynamic-program based
polynomial time algorithm which optimally computes a directed variant of the
influence maximization problem on hierarchical blockmodel networks. Our
algorithm indicates that the inapproximability result is due to the
bidirectionality of influence between agent-blocks.
Second, we present strong inapproximability results for a class of influence
functions that are "almost" submodular, called 2-quasi-submodular. Our
inapproximability results hold even for any 2-quasi-submodular fixed in
advance. This result also indicates that the "threshold" between submodularity
and nonsubmodularity is sharp, regarding the approximability of influence
maximization.Comment: 53 pages, 20 figures; Conference short version - WINE 2017: The 13th
Conference on Web and Internet Economics; Journal full version - ACM:
Transactions on Computation Theory, 201
Seeds Buffering for Information Spreading Processes
Seeding strategies for influence maximization in social networks have been
studied for more than a decade. They have mainly relied on the activation of
all resources (seeds) simultaneously in the beginning; yet, it has been shown
that sequential seeding strategies are commonly better. This research focuses
on studying sequential seeding with buffering, which is an extension to basic
sequential seeding concept. The proposed method avoids choosing nodes that will
be activated through the natural diffusion process, which is leading to better
use of the budget for activating seed nodes in the social influence process.
This approach was compared with sequential seeding without buffering and single
stage seeding. The results on both real and artificial social networks confirm
that the buffer-based consecutive seeding is a good trade-off between the final
coverage and the time to reach it. It performs significantly better than its
rivals for a fixed budget. The gain is obtained by dynamic rankings and the
ability to detect network areas with nodes that are not yet activated and have
high potential of activating their neighbours.Comment: Jankowski, J., Br\'odka, P., Michalski, R., & Kazienko, P. (2017,
September). Seeds Buffering for Information Spreading Processes. In
International Conference on Social Informatics (pp. 628-641). Springe
Parameterized Inapproximability of Target Set Selection and Generalizations
In this paper, we consider the Target Set Selection problem: given a graph
and a threshold value for any vertex of the graph, find a minimum
size vertex-subset to "activate" s.t. all the vertices of the graph are
activated at the end of the propagation process. A vertex is activated
during the propagation process if at least of its neighbors are
activated. This problem models several practical issues like faults in
distributed networks or word-to-mouth recommendations in social networks. We
show that for any functions and this problem cannot be approximated
within a factor of in time, unless FPT = W[P],
even for restricted thresholds (namely constant and majority thresholds). We
also study the cardinality constraint maximization and minimization versions of
the problem for which we prove similar hardness results
Probing Limits of Information Spread with Sequential Seeding
We consider here information spread which propagates with certain probability
from nodes just activated to their not yet activated neighbors. Diffusion
cascades can be triggered by activation of even a small set of nodes. Such
activation is commonly performed in a single stage. A novel approach based on
sequential seeding is analyzed here resulting in three fundamental
contributions. First, we propose a coordinated execution of randomized choices
to enable precise comparison of different algorithms in general. We apply it
here when the newly activated nodes at each stage of spreading attempt to
activate their neighbors. Then, we present a formal proof that sequential
seeding delivers at least as large coverage as the single stage seeding does.
Moreover, we also show that, under modest assumptions, sequential seeding
achieves coverage provably better than the single stage based approach using
the same number of seeds and node ranking. Finally, we present experimental
results showing how single stage and sequential approaches on directed and
undirected graphs compare to the well-known greedy approach to provide the
objective measure of the sequential seeding benefits. Surprisingly, applying
sequential seeding to a simple degree-based selection leads to higher coverage
than achieved by the computationally expensive greedy approach currently
considered to be the best heuristic
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