89,771 research outputs found
Online Influence Maximization in Non-Stationary Social Networks
Social networks have been popular platforms for information propagation. An
important use case is viral marketing: given a promotion budget, an advertiser
can choose some influential users as the seed set and provide them free or
discounted sample products; in this way, the advertiser hopes to increase the
popularity of the product in the users' friend circles by the world-of-mouth
effect, and thus maximizes the number of users that information of the
production can reach. There has been a body of literature studying the
influence maximization problem. Nevertheless, the existing studies mostly
investigate the problem on a one-off basis, assuming fixed known influence
probabilities among users, or the knowledge of the exact social network
topology. In practice, the social network topology and the influence
probabilities are typically unknown to the advertiser, which can be varying
over time, i.e., in cases of newly established, strengthened or weakened social
ties. In this paper, we focus on a dynamic non-stationary social network and
design a randomized algorithm, RSB, based on multi-armed bandit optimization,
to maximize influence propagation over time. The algorithm produces a sequence
of online decisions and calibrates its explore-exploit strategy utilizing
outcomes of previous decisions. It is rigorously proven to achieve an
upper-bounded regret in reward and applicable to large-scale social networks.
Practical effectiveness of the algorithm is evaluated using both synthetic and
real-world datasets, which demonstrates that our algorithm outperforms previous
stationary methods under non-stationary conditions.Comment: 10 pages. To appear in IEEE/ACM IWQoS 2016. Full versio
Cascading Behavior in Large Blog Graphs
How do blogs cite and influence each other? How do such links evolve? Does
the popularity of old blog posts drop exponentially with time? These are some
of the questions that we address in this work. Our goal is to build a model
that generates realistic cascades, so that it can help us with link prediction
and outlier detection.
Blogs (weblogs) have become an important medium of information because of
their timely publication, ease of use, and wide availability. In fact, they
often make headlines, by discussing and discovering evidence about political
events and facts. Often blogs link to one another, creating a publicly
available record of how information and influence spreads through an underlying
social network. Aggregating links from several blog posts creates a directed
graph which we analyze to discover the patterns of information propagation in
blogspace, and thereby understand the underlying social network. Not only are
blogs interesting on their own merit, but our analysis also sheds light on how
rumors, viruses, and ideas propagate over social and computer networks.
Here we report some surprising findings of the blog linking and information
propagation structure, after we analyzed one of the largest available datasets,
with 45,000 blogs and ~ 2.2 million blog-postings. Our analysis also sheds
light on how rumors, viruses, and ideas propagate over social and computer
networks. We also present a simple model that mimics the spread of information
on the blogosphere, and produces information cascades very similar to those
found in real life
The dynamical strength of social ties in information spreading
We investigate the temporal patterns of human communication and its influence
on the spreading of information in social networks. The analysis of mobile
phone calls of 20 million people in one country shows that human communication
is bursty and happens in group conversations. These features have opposite
effects in information reach: while bursts hinder propagation at large scales,
conversations favor local rapid cascades. To explain these phenomena we define
the dynamical strength of social ties, a quantity that encompasses both the
topological and temporal patterns of human communication
Implementation of a Social Network Information Dissemination Model Incorporating Negative Relationships
For the study of information dissemination in online social networks, most existing information dissemination models include only positive relationships, ignoring the existence and importance of negative relationships, and do not consider the influence of inter-individual relationship polarity on dissemination. To solve these problems, we propose a social network information dissemination model incorporating negative relationships in this paper. Drawing on the state concept of the SIR (Susceptible Infected Recovered) model, the three types of SIR states are subdivided into five sub-states. Combining the advantages of the viewpoint evolution model, the influence of relational polarity on node attitudes is added to the modeling of the propagation process. The experiment proves that the method proposed in this paper can show more specifically the changing trend in the number of propagation nodes with different attitudes and portray the process of information propagation in online social networks
How to Network in Online Social Networks
In this paper, we consider how to maximize users' influence in Online Social
Networks (OSNs) by exploiting social relationships only. Our first contribution
is to extend to OSNs the model of Kempe et al. [1] on the propagation of
information in a social network and to show that a greedy algorithm is a good
approximation of the optimal algorithm that is NP-hard. However, the greedy
algorithm requires global knowledge, which is hardly practical. Our second
contribution is to show on simulations on the full Twitter social graph that
simple and practical strategies perform close to the greedy algorithm.Comment: NetSciCom 2014 - The Sixth IEEE International Workshop on Network
Science for Communication Networks (2014
Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation
- …