6,550 research outputs found
Revisiting Non-Progressive Influence Models: Scalable Influence Maximization
While influence maximization in social networks has been studied extensively
in computer science community for the last decade the focus has been on the
progressive influence models, such as independent cascade (IC) and Linear
threshold (LT) models, which cannot capture the reversibility of choices. In
this paper, we present the Heat Conduction (HC) model which is a
non-progressive influence model with real-world interpretations. We show that
HC unifies, generalizes, and extends the existing nonprogressive models, such
as the Voter model [1] and non-progressive LT [2]. We then prove that selecting
the optimal seed set of influential nodes is NP-hard for HC but by establishing
the submodularity of influence spread, we can tackle the influence maximization
problem with a scalable and provably near-optimal greedy algorithm. We are the
first to present a scalable solution for influence maximization under
nonprogressive LT model, as a special case of the HC model. In sharp contrast
to the other greedy influence maximization methods, our fast and efficient
C2GREEDY algorithm benefits from two analytically computable steps: closed-form
computation for finding the influence spread as well as the greedy seed
selection. Through extensive experiments on several large real and synthetic
networks, we show that C2GREEDY outperforms the state-of-the-art methods, in
terms of both influence spread and scalability.Comment: G. Golnari and A. Asiaee contributed equally to this work. Published
in 31st Conference on Uncertainty in Artificial Intelligence (UAI) proceedin
Modeling Non-Progressive Phenomena for Influence Propagation
Recent work on modeling influence propagation focus on progressive models,
i.e., once a node is influenced (active) the node stays in that state and
cannot become inactive. However, this assumption is unrealistic in many
settings where nodes can transition between active and inactive states. For
instance, a user of a social network may stop using an app and become inactive,
but again activate when instigated by a friend, or when the app adds a new
feature or releases a new version. In this work, we study such non-progressive
phenomena and propose an efficient model of influence propagation.
Specifically, we model in influence propagation as a continuous-time Markov
process with 2 states: active and inactive. Such a model is both highly
scalable (we evaluated on graphs with over 2 million nodes), 17-20 times
faster, and more accurate for estimating the spread of influence, as compared
with state-of-the-art progressive models for several applications where nodes
may switch states
New Insights from an Analysis of Social Influence Networks under the Linear Threshold Model
We study the spread of influence in a social network based on the Linear
Threshold model. We derive an analytical expression for evaluating the expected
size of the eventual influenced set for a given initial set, using the
probability of activation for each node in the social network. We then provide
an equivalent interpretation for the influence spread, in terms of acyclic path
probabilities in the Markov chain obtained by reversing the edges in the social
network influence graph. We use some properties of such acyclic path
probabilities to provide an alternate proof for the submodularity of the
influence function. We illustrate the usefulness of the analytical expression
in estimating the most influential set, in special cases such as the
UILT(Uniform Influence Linear Threshold), USLT(Uniform Susceptance Linear
Threshold) and node-degree based influence models. We show that the PageRank
heuristic is either provably optimal or performs very well in the above models,
and explore its limitations in more general cases. Finally, based on the
insights obtained from the analytical expressions, we provide an efficient
algorithm which approximates the greedy algorithm for the influence
maximization problem.Comment: 13 pages, 6 figure
Model-Independent Online Learning for Influence Maximization
We consider influence maximization (IM) in social networks, which is the
problem of maximizing the number of users that become aware of a product by
selecting a set of "seed" users to expose the product to. While prior work
assumes a known model of information diffusion, we propose a novel
parametrization that not only makes our framework agnostic to the underlying
diffusion model, but also statistically efficient to learn from data. We give a
corresponding monotone, submodular surrogate function, and show that it is a
good approximation to the original IM objective. We also consider the case of a
new marketer looking to exploit an existing social network, while
simultaneously learning the factors governing information propagation. For
this, we propose a pairwise-influence semi-bandit feedback model and develop a
LinUCB-based bandit algorithm. Our model-independent analysis shows that our
regret bound has a better (as compared to previous work) dependence on the size
of the network. Experimental evaluation suggests that our framework is robust
to the underlying diffusion model and can efficiently learn a near-optimal
solution
On the Non-Progressive Spread of Influence through Social Networks
The spread of influence in social networks is studied in two main categories:
the progressive model and the non-progressive model (see e.g. the seminal work
of Kempe, Kleinberg, and Tardos in KDD 2003). While the progressive models are
suitable for modeling the spread of influence in monopolistic settings,
non-progressive are more appropriate for modeling non-monopolistic settings,
e.g., modeling diffusion of two competing technologies over a social network.
Despite the extensive work on the progressive model, non-progressive models
have not been studied well. In this paper, we study the spread of influence in
the non-progressive model under the strict majority threshold: given a graph
with a set of initially infected nodes, each node gets infected at time
iff a majority of its neighbors are infected at time . Our goal
in the \textit{MinPTS} problem is to find a minimum-cardinality initial set of
infected nodes that would eventually converge to a steady state where all nodes
of are infected.
We prove that while the MinPTS is NP-hard for a restricted family of graphs,
it admits an improved constant-factor approximation algorithm for power-law
graphs. We do so by proving lower and upper bounds in terms of the minimum and
maximum degree of nodes in the graph. The upper bound is achieved in turn by
applying a natural greedy algorithm. Our experimental evaluation of the greedy
algorithm also shows its superior performance compared to other algorithms for
a set of real-world graphs as well as the random power-law graphs. Finally, we
study the convergence properties of these algorithms and show that the
non-progressive model converges in at most steps
Time is What Prevents Everything from Happening at Once: Propagation Time-conscious Influence Maximization
The influence maximization (IM) problem as defined in the seminal paper by
Kempe et al. has received widespread attention from various research
communities, leading to the design of a wide variety of solutions.
Unfortunately, this classical IM problem ignores the fact that time taken for
influence propagation to reach the largest scope can be significant in
realworld social networks, during which the underlying network itself may have
evolved. This phenomenon may have considerable adverse impact on the quality of
selected seeds and as a result all existing techniques that use this classical
definition as their building block generate seeds with suboptimal influence
spread. In this paper, we revisit the classical IM problem and propose a more
realistic version called PROTEUS-IM (Propagation Time conscious Influence
Maximization) to replace it by addressing the aforementioned limitation.
Specifically, as influence propagation may take time, we assume that the
underlying social network may evolve during influence propagation.
Consequently, PROTEUSIM aims to select seeds in the current network to maximize
influence spread in the future instance of the network at the end of influence
propagation process without assuming complete topological knowledge of the
future network. We propose a greedy and a Reverse Reachable (RR) set-based
algorithms called PROTEUS-GENIE and PROTEUS-SEER, respectively, to address this
problem. Our algorithms utilize the state-of-the-art Forest Fire Model for
modeling network evolution during influence propagation to find superior
quality seeds. Experimental study on real and synthetic social networks shows
that our proposed algorithms consistently outperform state-of-the-art classical
IM algorithms with respect to seed set quality.Comment: 14 pages, 26 figure
Time-Bounded Influence Diffusion with Incentives
A widely studied model of influence diffusion in social networks represents
the network as a graph with an influence threshold for each
node. Initially the members of an initial set are influenced.
During each subsequent round, the set of influenced nodes is augmented by
including every node that has at least previously influenced
neighbours. The general problem is to find a small initial set that influences
the whole network. In this paper we extend this model by using
\emph{incentives} to reduce the thresholds of some nodes. The goal is to
minimize the total of the incentives required to ensure that the process
completes within a given number of rounds. The problem is hard to approximate
in general networks. We present polynomial-time algorithms for paths, trees,
and complete networks.Comment: An extended abstract of this paper was presented at the 25th
International Colloquium on Structural Information and Communication
Complexity (Sirocco 2018) June 18-21, 2018 Ma'ale HaHamisha, Israe
Trust-based dynamic linear threshold models for non-competitive and competitive influence propagation
What are the key-features that enable an information diffusion model to
explain the inherent dynamic, and often competitive, nature of real-world
propagation phenomena? In this paper we aim to answer this question by
proposing a novel class of diffusion models, inspired by the classic Linear
Threshold model, and built around the following aspects: trust/distrust in the
user relationships, which is leveraged to model different effects of social
influence on the decisions taken by an individual; changes in adopting one or
alternative information items; hesitation towards adopting an information item
over time; latency in the propagation; time horizon for the unfolding of the
diffusion process; and multiple cascades of information that might occur
competitively. To the best of our knowledge, the above aspects have never been
unified into the same LT-based diffusion model. We also define different
strategies for the selection of the initial influencers to simulate
non-competitive and competitive diffusion scenarios, particularly related to
the problem of limitation of misinformation spread. Results on publicly
available networks have shown the meaningfulness and uniqueness of our models.Comment: Accepted (May 5, 2018) at the IEEE TrustCom/BigDataSE 2018 Conferenc
Complex influence propagation based on trust-aware dynamic linear threshold models
Abstract To properly capture the complexity of influence propagation phenomena in real-world contexts, such as those related to viral marketing and misinformation spread, information diffusion models should fulfill a number of requirements. These include accounting for several dynamic aspects in the propagation (e.g., latency, time horizon), dealing with multiple cascades of information that might occur competitively, accounting for the contingencies that lead a user to change her/his adoption of one or alternative information items, and leveraging trust/distrust in the users' relationships and its effect of influence on the users' decisions. To the best of our knowledge, no diffusion model unifying all of the above requirements has been developed so far. In this work, we address such a challenge and propose a novel class of diffusion models, inspired by the classic linear threshold model, which are designed to deal with trust-aware, non-competitive as well as competitive time-varying propagation scenarios. Our theoretical inspection of the proposed models unveils important findings on the relations with existing linear threshold models for which properties are known about whether monotonicity and submodularity hold for the corresponding activation function. We also propose strategies for the selection of the initial spreaders of the propagation process, for both non-competitive and competitive influence propagation tasks, whose goal is to mimic contexts of misinformation spread. Our extensive experimental evaluation, which was conducted on publicly available networks and included comparison with competing methods, provides evidence on the meaningfulness and uniqueness of our models
Influence Spread in Social Networks: A Study via a Fluid Limit of the Linear Threshold Model
Threshold based models have been widely used in characterizing collective
behavior on social networks. An individual's threshold indicates the minimum
level of influence that must be exerted, by other members of the population
engaged in some activity, before the individual will join the activity. In this
work, we begin with a homogeneous version of the Linear Threshold model
proposed by Kempe et al. in the context of viral marketing, and generalize this
model to arbitrary threshold distributions. We show that the evolution can be
modeled as a discrete time Markov chain, and, by using a certain scaling, we
obtain a fluid limit that provides an ordinary differential equation model
(o.d.e.). We find that the threshold distribution appears in the o.d.e. via its
hazard rate function. We demonstrate the accuracy of the o.d.e. approximation
and derive explicit expressions for the trajectory of influence under the
uniform threshold distribution. Also, for an exponentially distributed
threshold, we show that the fluid dynamics are equivalent to the well-known SIR
model in epidemiology. We also numerically study how other hazard functions
(obtained from the Weibull and loglogistic distributions) provide qualitative
different characteristics of the influence evolution, compared to traditional
epidemic models, even in a homogeneous setting. We finally show how the model
can be extended to a setting with multiple communities and conclude with
possible future directions
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