692 research outputs found
Evolution of the Media Web
We present a detailed study of the part of the Web related to media content,
i.e., the Media Web. Using publicly available data, we analyze the evolution of
incoming and outgoing links from and to media pages. Based on our observations,
we propose a new class of models for the appearance of new media content on the
Web where different \textit{attractiveness} functions of nodes are possible
including ones taken from well-known preferential attachment and fitness
models. We analyze these models theoretically and empirically and show which
ones realistically predict both the incoming degree distribution and the
so-called \textit{recency property} of the Media Web, something that existing
models did not do well. Finally we compare these models by estimating the
likelihood of the real-world link graph from our data set given each model and
obtain that models we introduce are significantly more likely than previously
proposed ones. One of the most surprising results is that in the Media Web the
probability for a post to be cited is determined, most likely, by its quality
rather than by its current popularity
Emergence of social networks via direct and indirect reciprocity
Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals' degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to
strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores ("indirect reciprocity"), which is known to play an important role in many economic interactions. In
order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. "tit-for-tat") as well as indirect
reciprocity (helping strangers in order to increase one's reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which
are dynamic at the individual level but stable at the network level
Implications of Computational Cognitive Models for Information Retrieval
This dissertation explores the implications of computational cognitive modeling for information retrieval. The parallel between information retrieval and human memory is that the goal of an information retrieval system is to find the set of documents most relevant to the query whereas the goal for the human memory system is to access the relevance of items stored in memory given a memory probe (Steyvers & Griffiths, 2010).
The two major topics of this dissertation are desirability and information scent. Desirability is the context independent probability of an item receiving attention (Recker & Pitkow, 1996). Desirability has been widely utilized in numerous experiments to model the probability that a given memory item would be retrieved (Anderson, 2007). Information scent is a context dependent measure defined as the utility of an information item (Pirolli & Card, 1996b). Information scent has been widely utilized to predict the memory item that would be retrieved given a probe (Anderson, 2007) and to predict the browsing behavior of humans (Pirolli & Card, 1996b).
In this dissertation, I proposed the theory that desirability observed in human memory is caused by preferential attachment in networks. Additionally, I showed that documents accessed in large repositories mirror the observed statistical properties in human memory and that these properties can be used to improve document ranking. Finally, I showed that the combination of information scent and desirability improves document ranking over existing well-established approaches
The Effect of Recency to Human Mobility
In recent years, we have seen scientists attempt to model and explain human
dynamics and, in particular, human movement. Many aspects of our complex life
are affected by human movements such as disease spread and epidemics modeling,
city planning, wireless network development, and disaster relief, to name a
few. Given the myriad of applications it is clear that a complete understanding
of how people move in space can lead to huge benefits to our society. In most
of the recent works, scientists have focused on the idea that people movements
are biased towards frequently-visited locations. According to them, human
movement is based on an exploration/exploitation dichotomy in which individuals
choose new locations (exploration) or return to frequently-visited locations
(exploitation). In this work, we focus on the concept of recency. We propose a
model in which exploitation in human movement also considers recently-visited
locations and not solely frequently-visited locations. We test our hypothesis
against different empirical data of human mobility and show that our proposed
model is able to better explain the human trajectories in these datasets
Recency predicts bursts in the evolution of author citations
The citations process for scientific papers has been studied extensively. But
while the citations accrued by authors are the sum of the citations of their
papers, translating the dynamics of citation accumulation from the paper to the
author level is not trivial. Here we conduct a systematic study of the
evolution of author citations, and in particular their bursty dynamics. We find
empirical evidence of a correlation between the number of citations most
recently accrued by an author and the number of citations they receive in the
future. Using a simple model where the probability for an author to receive new
citations depends only on the number of citations collected in the previous
12-24 months, we are able to reproduce both the citation and burst size
distributions of authors across multiple decades.Comment: 12 pages, 7 figure
A Relational Event Approach to Modeling Behavioral Dynamics
This chapter provides an introduction to the analysis of relational event
data (i.e., actions, interactions, or other events involving multiple actors
that occur over time) within the R/statnet platform. We begin by reviewing the
basics of relational event modeling, with an emphasis on models with piecewise
constant hazards. We then discuss estimation for dyadic and more general
relational event models using the relevent package, with an emphasis on
hands-on applications of the methods and interpretation of results. Statnet is
a collection of packages for the R statistical computing system that supports
the representation, manipulation, visualization, modeling, simulation, and
analysis of relational data. Statnet packages are contributed by a team of
volunteer developers, and are made freely available under the GNU Public
License. These packages are written for the R statistical computing
environment, and can be used with any computing platform that supports R
(including Windows, Linux, and Mac).
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