1,478 research outputs found
Seeding with Costly Network Information
We study the task of selecting nodes in a social network of size , to
seed a diffusion with maximum expected spread size, under the independent
cascade model with cascade probability . Most of the previous work on this
problem (known as influence maximization) focuses on efficient algorithms to
approximate the optimal seed set with provable guarantees, given the knowledge
of the entire network. However, in practice, obtaining full knowledge of the
network is very costly. To address this gap, we first study the achievable
guarantees using influence samples. We provide an approximation
algorithm with a tight (1-1/e){\mbox{OPT}}-\epsilon n guarantee, using
influence samples and show that this dependence on
is asymptotically optimal. We then propose a probing algorithm that queries
edges from the graph and use them to find a seed set with the
same almost tight approximation guarantee. We also provide a matching (up to
logarithmic factors) lower-bound on the required number of edges. To address
the dependence of our probing algorithm on the independent cascade probability
, we show that it is impossible to maintain the same approximation
guarantees by controlling the discrepancy between the probing and seeding
cascade probabilities. Instead, we propose to down-sample the probed edges to
match the seeding cascade probability, provided that it does not exceed that of
probing. Finally, we test our algorithms on real world data to quantify the
trade-off between the cost of obtaining more refined network information and
the benefit of the added information for guiding improved seeding strategies
Infection Spreading and Source Identification: A Hide and Seek Game
The goal of an infection source node (e.g., a rumor or computer virus source)
in a network is to spread its infection to as many nodes as possible, while
remaining hidden from the network administrator. On the other hand, the network
administrator aims to identify the source node based on knowledge of which
nodes have been infected. We model the infection spreading and source
identification problem as a strategic game, where the infection source and the
network administrator are the two players. As the Jordan center estimator is a
minimax source estimator that has been shown to be robust in recent works, we
assume that the network administrator utilizes a source estimation strategy
that can probe any nodes within a given radius of the Jordan center. Given any
estimation strategy, we design a best-response infection strategy for the
source. Given any infection strategy, we design a best-response estimation
strategy for the network administrator. We derive conditions under which a Nash
equilibrium of the strategic game exists. Simulations in both synthetic and
real-world networks demonstrate that our proposed infection strategy infects
more nodes while maintaining the same safety margin between the true source
node and the Jordan center source estimator
Influence Maximization in Social Networks: A Survey
Online social networks have become an important platform for people to
communicate, share knowledge and disseminate information. Given the widespread
usage of social media, individuals' ideas, preferences and behavior are often
influenced by their peers or friends in the social networks that they
participate in. Since the last decade, influence maximization (IM) problem has
been extensively adopted to model the diffusion of innovations and ideas. The
purpose of IM is to select a set of k seed nodes who can influence the most
individuals in the network.
In this survey, we present a systematical study over the researches and
future directions with respect to IM problem. We review the information
diffusion models and analyze a variety of algorithms for the classic IM
algorithms. We propose a taxonomy for potential readers to understand the key
techniques and challenges. We also organize the milestone works in time order
such that the readers of this survey can experience the research roadmap in
this field. Moreover, we also categorize other application-oriented IM studies
and correspondingly study each of them. What's more, we list a series of open
questions as the future directions for IM-related researches, where a potential
reader of this survey can easily observe what should be done next in this
field
Big Networks: Analysis and Optimal Control
The study of networks has seen a tremendous breed of researches due to the explosive spectrum of practical problems that involve networks as the access point. Those problems widely range from detecting functionally correlated proteins in biology to finding people to give discounts and gain maximum popularity of a product in economics. Thus, understanding and further being able to manipulate/control the development and evolution of the networks become critical tasks for network scientists. Despite the vast research effort putting towards these studies, the present state-of-the-arts largely either lack of high quality solutions or require excessive amount of time in real-world `Big Data\u27 requirement.
This research aims at affirmatively boosting the modern algorithmic efficiency to approach practical requirements. That is developing a ground-breaking class of algorithms that provide simultaneously both provably good solution qualities and low time and space complexities. Specifically, I target the important yet challenging problems in the three main areas:
Information Diffusion: Analyzing and maximizing the influence in networks and extending results for different variations of the problems.
Community Detection: Finding communities from multiple sources of information.
Security and Privacy: Assessing organization vulnerability under targeted-cyber attacks via social networks
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