137,115 research outputs found
Seeds Buffering for Information Spreading Processes
Seeding strategies for influence maximization in social networks have been
studied for more than a decade. They have mainly relied on the activation of
all resources (seeds) simultaneously in the beginning; yet, it has been shown
that sequential seeding strategies are commonly better. This research focuses
on studying sequential seeding with buffering, which is an extension to basic
sequential seeding concept. The proposed method avoids choosing nodes that will
be activated through the natural diffusion process, which is leading to better
use of the budget for activating seed nodes in the social influence process.
This approach was compared with sequential seeding without buffering and single
stage seeding. The results on both real and artificial social networks confirm
that the buffer-based consecutive seeding is a good trade-off between the final
coverage and the time to reach it. It performs significantly better than its
rivals for a fixed budget. The gain is obtained by dynamic rankings and the
ability to detect network areas with nodes that are not yet activated and have
high potential of activating their neighbours.Comment: Jankowski, J., Br\'odka, P., Michalski, R., & Kazienko, P. (2017,
September). Seeds Buffering for Information Spreading Processes. In
International Conference on Social Informatics (pp. 628-641). Springe
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation
Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches
Explaining Snapshots of Network Diffusions: Structural and Hardness Results
Much research has been done on studying the diffusion of ideas or
technologies on social networks including the \textit{Influence Maximization}
problem and many of its variations. Here, we investigate a type of inverse
problem. Given a snapshot of the diffusion process, we seek to understand if
the snapshot is feasible for a given dynamic, i.e., whether there is a limited
number of nodes whose initial adoption can result in the snapshot in finite
time. While similar questions have been considered for epidemic dynamics, here,
we consider this problem for variations of the deterministic Linear Threshold
Model, which is more appropriate for modeling strategic agents. Specifically,
we consider both sequential and simultaneous dynamics when deactivations are
allowed and when they are not. Even though we show hardness results for all
variations we consider, we show that the case of sequential dynamics with
deactivations allowed is significantly harder than all others. In contrast,
sequential dynamics make the problem trivial on cliques even though it's
complexity for simultaneous dynamics is unknown. We complement our hardness
results with structural insights that can help better understand diffusions of
social networks under various dynamics.Comment: 14 pages, 3 figure
Diffusion of Lexical Change in Social Media
Computer-mediated communication is driving fundamental changes in the nature
of written language. We investigate these changes by statistical analysis of a
dataset comprising 107 million Twitter messages (authored by 2.7 million unique
user accounts). Using a latent vector autoregressive model to aggregate across
thousands of words, we identify high-level patterns in diffusion of linguistic
change over the United States. Our model is robust to unpredictable changes in
Twitter's sampling rate, and provides a probabilistic characterization of the
relationship of macro-scale linguistic influence to a set of demographic and
geographic predictors. The results of this analysis offer support for prior
arguments that focus on geographical proximity and population size. However,
demographic similarity -- especially with regard to race -- plays an even more
central role, as cities with similar racial demographics are far more likely to
share linguistic influence. Rather than moving towards a single unified
"netspeak" dialect, language evolution in computer-mediated communication
reproduces existing fault lines in spoken American English.Comment: preprint of PLOS-ONE paper from November 2014; PLoS ONE 9(11) e11311
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