31,961 research outputs found
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
LATTE: Application Oriented Social Network Embedding
In recent years, many research works propose to embed the network structured
data into a low-dimensional feature space, where each node is represented as a
feature vector. However, due to the detachment of embedding process with
external tasks, the learned embedding results by most existing embedding models
can be ineffective for application tasks with specific objectives, e.g.,
community detection or information diffusion. In this paper, we propose study
the application oriented heterogeneous social network embedding problem.
Significantly different from the existing works, besides the network structure
preservation, the problem should also incorporate the objectives of external
applications in the objective function. To resolve the problem, in this paper,
we propose a novel network embedding framework, namely the "appLicAtion
orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network
structure can be applied to compute the node "diffusive proximity" scores,
which capture both local and global network structures. Based on these computed
scores, Latte learns the network representation feature vectors by extending
the autoencoder model model to the heterogeneous network scenario, which can
also effectively unite the objectives of network embedding and external
application tasks. Extensive experiments have been done on real-world
heterogeneous social network datasets, and the experimental results have
demonstrated the outstanding performance of Latte in learning the
representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl
An Integrated Framework for Competitive Multi-channel Marketing of Multi-featured Products
For any company, multiple channels are available for reaching a population in
order to market its products. Some of the most well-known channels are (a) mass
media advertisement, (b) recommendations using social advertisement, and (c)
viral marketing using social networks. The company would want to maximize its
reach while also accounting for simultaneous marketing of competing products,
where the product marketings may not be independent. In this direction, we
propose and analyze a multi-featured generalization of the classical linear
threshold model. We hence develop a framework for integrating the considered
marketing channels into the social network, and an approach for allocating
budget among these channels
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
When-To-Post on Social Networks
For many users on social networks, one of the goals when broadcasting content
is to reach a large audience. The probability of receiving reactions to a
message differs for each user and depends on various factors, such as location,
daily and weekly behavior patterns and the visibility of the message. While
previous work has focused on overall network dynamics and message flow
cascades, the problem of recommending personalized posting times has remained
an underexplored topic of research. In this study, we formulate a when-to-post
problem, where the objective is to find the best times for a user to post on
social networks in order to maximize the probability of audience responses. To
understand the complexity of the problem, we examine user behavior in terms of
post-to-reaction times, and compare cross-network and cross-city weekly
reaction behavior for users in different cities, on both Twitter and Facebook.
We perform this analysis on over a billion posted messages and observed
reactions, and propose multiple approaches for generating personalized posting
schedules. We empirically assess these schedules on a sampled user set of 0.5
million active users and more than 25 million messages observed over a 56 day
period. We show that users see a reaction gain of up to 17% on Facebook and 4%
on Twitter when the recommended posting times are used. We open the dataset
used in this study, which includes timestamps for over 144 million posts and
over 1.1 billion reactions. The personalized schedules derived here are used in
a fully deployed production system to recommend posting times for millions of
users every day.Comment: 10 pages, to appear in KDD201
Latent Self-Exciting Point Process Model for Spatial-Temporal Networks
We propose a latent self-exciting point process model that describes
geographically distributed interactions between pairs of entities. In contrast
to most existing approaches that assume fully observable interactions, here we
consider a scenario where certain interaction events lack information about
participants. Instead, this information needs to be inferred from the available
observations. We develop an efficient approximate algorithm based on
variational expectation-maximization to infer unknown participants in an event
given the location and the time of the event. We validate the model on
synthetic as well as real-world data, and obtain very promising results on the
identity-inference task. We also use our model to predict the timing and
participants of future events, and demonstrate that it compares favorably with
baseline approaches.Comment: 20 pages, 6 figures (v3); 11 pages, 6 figures (v2); previous version
appeared in the 9th Bayesian Modeling Applications Workshop, UAI'1
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
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