3,570 research outputs found
Selecting Nodes and Buying Links to Maximize the Information Diffusion in a Network
The Independent Cascade Model (ICM) is a widely studied model that aims to capture the dynamics of the information diffusion in social networks and in general complex networks. In this model, we can distinguish between active nodes which spread the information and inactive ones. The process starts from a set of initially active nodes called seeds. Recursively, currently active nodes can activate their neighbours according to a probability distribution on the set of edges. After a certain number of these recursive cycles, a large number of nodes might become active. The process terminates when no further node gets activated.
Starting from the work of Domingos and Richardson [Domingos et al. 2001], several studies have been conducted with the aim of shaping a given diffusion process so as to maximize the number of activated nodes at the end of the process. One of the most studied problems has been formalized by Kempe et al. and consists in finding a set of initial seeds that maximizes the expected number of active nodes under a budget constraint [Kempe et al. 2003].
In this paper we study a generalization of the problem of Kempe et al. in which we are allowed to spend part of the budget to create new edges incident to the seeds. That is, the budget can be spent to buy seeds or edges according to a cost function. The problem does not admin a PTAS, unless P=NP. We propose two approximation algorithms: the former one gives an approximation ratio that depends on the edge costs and increases when these costs are high; the latter algorithm gives a constant approximation guarantee which is greater than that of the first algorithm when the edge costs can be small
When Social Influence Meets Item Inference
Research issues and data mining techniques for product recommendation and
viral marketing have been widely studied. Existing works on seed selection in
social networks do not take into account the effect of product recommendations
in e-commerce stores. In this paper, we investigate the seed selection problem
for viral marketing that considers both effects of social influence and item
inference (for product recommendation). We develop a new model, Social Item
Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we
formulate a seed selection problem, called Social Item Maximization Problem
(SIMP), and prove the hardness of SIMP. We design an efficient algorithm with
performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and
develop a new index structure, called SIG-index, to accelerate the computation
of diffusion process in HAG. Moreover, to construct realistic SIG models for
SIMP, we develop a statistical inference based framework to learn the weights
of hyperedges from data. Finally, we perform a comprehensive evaluation on our
proposals with various baselines. Experimental result validates our ideas and
demonstrates the effectiveness and efficiency of the proposed model and
algorithms over baselines.Comment: 12 page
From Competition to Complementarity: Comparative Influence Diffusion and Maximization
Influence maximization is a well-studied problem that asks for a small set of
influential users from a social network, such that by targeting them as early
adopters, the expected total adoption through influence cascades over the
network is maximized. However, almost all prior work focuses on cascades of a
single propagating entity or purely-competitive entities. In this work, we
propose the Comparative Independent Cascade (Com-IC) model that covers the full
spectrum of entity interactions from competition to complementarity. In Com-IC,
users' adoption decisions depend not only on edge-level information
propagation, but also on a node-level automaton whose behavior is governed by a
set of model parameters, enabling our model to capture not only competition,
but also complementarity, to any possible degree. We study two natural
optimization problems, Self Influence Maximization and Complementary Influence
Maximization, in a novel setting with complementary entities. Both problems are
NP-hard, and we devise efficient and effective approximation algorithms via
non-trivial techniques based on reverse-reachable sets and a novel "sandwich
approximation". The applicability of both techniques extends beyond our model
and problems. Our experiments show that the proposed algorithms consistently
outperform intuitive baselines in four real-world social networks, often by a
significant margin. In addition, we learn model parameters from real user
action logs.Comment: An abridged of this work is to appear in the Proceedings of VLDB
Endowment (PVDLB), Vol 9, No 2. Also, the paper will be presented in the VLDB
2016 conference in New Delhi, India. This update contains new theoretical and
experimental results, and the paper is now in single-column format (44 pages
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization
In this paper, we propose the amphibious influence maximization (AIM) model
that combines traditional marketing via content providers and viral marketing
to consumers in social networks in a single framework. In AIM, a set of content
providers and consumers form a bipartite network while consumers also form
their social network, and influence propagates from the content providers to
consumers and among consumers in the social network following the independent
cascade model. An advertiser needs to select a subset of seed content providers
and a subset of seed consumers, such that the influence from the seed providers
passing through the seed consumers could reach a large number of consumers in
the social network in expectation.
We prove that the AIM problem is NP-hard to approximate to within any
constant factor via a reduction from Feige's k-prover proof system for 3-SAT5.
We also give evidence that even when the social network graph is trivial (i.e.
has no edges), a polynomial time constant factor approximation for AIM is
unlikely. However, when we assume that the weighted bi-adjacency matrix that
describes the influence of content providers on consumers is of constant rank,
a common assumption often used in recommender systems, we provide a
polynomial-time algorithm that achieves approximation ratio of
for any (polynomially small) . Our
algorithmic results still hold for a more general model where cascades in
social network follow a general monotone and submodular function.Comment: An extended abstract appeared in the Proceedings of the 16th ACM
Conference on Economics and Computation (EC), 201
Effects of Time Horizons on Influence Maximization in the Voter Dynamics
In this paper we analyze influence maximization in the voter model with an
active strategic and a passive influencing party in non-stationary settings. We
thus explore the dependence of optimal influence allocation on the time
horizons of the strategic influencer. We find that on undirected heterogeneous
networks, for short time horizons, influence is maximized when targeting
low-degree nodes, while for long time horizons influence maximization is
achieved when controlling hub nodes. Furthermore, we show that for short and
intermediate time scales influence maximization can exploit knowledge of
(transient) opinion configurations. More in detail, we find two rules. First,
nodes with states differing from the strategic influencer's goal should be
targeted. Second, if only few nodes are initially aligned with the strategic
influencer, nodes subject to opposing influence should be avoided, but when
many nodes are aligned, an optimal influencer should shadow opposing influence.Comment: 22 page
Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model
Motivated by applications such as viral marketing, the problem of influence
maximization (IM) has been extensively studied in the literature. The goal is
to select a small number of users to adopt an item such that it results in a
large cascade of adoptions by others. Existing works have three key
limitations. (1) They do not account for economic considerations of a user in
buying/adopting items. (2) Most studies on multiple items focus on competition,
with complementary items receiving limited attention. (3) For the network
owner, maximizing social welfare is important to ensure customer loyalty, which
is not addressed in prior work in the IM literature. In this paper, we address
all three limitations and propose a novel model called UIC that combines
utility-driven item adoption with influence propagation over networks. Focusing
on the mutually complementary setting, we formulate the problem of social
welfare maximization in this novel setting. We show that while the objective
function is neither submodular nor supermodular, surprisingly a simple greedy
allocation algorithm achieves a factor of of the optimum
expected social welfare. We develop \textsf{bundleGRD}, a scalable version of
this approximation algorithm, and demonstrate, with comprehensive experiments
on real and synthetic datasets, that it significantly outperforms all
baselines.Comment: 33 page
Integrating Social Network Effects in Product Design and Diffusion
Connectivities among people are amplified with recent advancements in internet technology increasing the number of communication channels. Information spread over these networks strengthen the social influence among individuals and affect their purchasing decisions. In this thesis, we study three problems in the product design and diffusion context by integrating such social network effects where influence takes place over neighborhood relationship ties among the users of the product. We consider the setting where peer influence plays a significant role in a consumer's product choice or there is a tangible benefit from using the same product as the rest of one's social network.
Building upon the well-known Share-of-Choice problem, we model an influence structure and define the Share-of-Choice problem with Network Effects. It is an NP-Hard combinatorial optimization problem which we solve using a Genetic Algorithm. Using simulated data we show that ignoring social network effects in the design phase of a product results in a significantly lower market share for a product. Our genetic algorithm obtains near-optimal solutions and is very robust in terms of its running time, scalability, and ability to adapt to additional constraints/variations of the model. In this setting, we introduce a product diffusion problem, the Least Cost Influence Problem, which increases the market share of a product by intervening the natural diffusion of it over the social network. This intervention is in the form of incentive supply to a group of people in a least costly way while maximizing the spread of the product.
We generalize the Least Cost Influence Problem by moving away from the marketing setting and by treating the previous product as any piece of "information" that can spread over a social network by adoption. We show that this problem is polynomially solvable over tree networks under some conditions. We provide a Dynamic Programming algorithm to solve this problem and show that it can be interpreted as a greedy algorithm that gives incentives starting with the people that are least influenced by their neighbors, albeit the definition of susceptibility to influence from neighbors is updated throughout the algorithm.
We introduce a two dimensional influence model and extend our modeling and solution methods for the product line design problem which involves designing multiple products within the same product line with the objective of appealing to the heterogeneous structure of the market. The first dimension of influence is the affection of individuals from using the same product, and the second dimension is the influence of using a similar product from the same product line which has a lower intensity of influence. We reexamine the Least Cost Influence Problem in the product line setting
- …