8,766 research outputs found
Mining Time-aware Actor-level Evolution Similarity for Link Prediction in Dynamic Network
Topological evolution over time in a dynamic network triggers both the addition and deletion of actors and the links among them. A dynamic network can be represented as a time series of network snapshots where each snapshot represents the state of the network over an interval of time (for example, a minute, hour or day). The duration of each snapshot denotes the temporal scale/sliding window of the dynamic network and all the links within the duration of the window are aggregated together irrespective of their order in time. The inherent trade-off in selecting the timescale in analysing dynamic networks is that choosing a short temporal window may lead to chaotic changes in network topology and measures (for example, the actors’ centrality measures and the average path length); however, choosing a long window may compromise the study and the investigation of network dynamics. Therefore, to facilitate the analysis and understand different patterns of actor-oriented evolutionary aspects, it is necessary to define an optimal window length (temporal duration) with which to sample a dynamic network. In addition to determining the optical temporal duration, another key task for understanding the dynamics of evolving networks is being able to predict the likelihood of future links among pairs of actors given the existing states of link structure at present time. This phenomenon is known as the link prediction problem in network science. Instead of considering a static state of a network where the associated topology does not change, dynamic link prediction attempts to predict emerging links by considering different types of historical/temporal information, for example the different types of temporal evolutions experienced by the actors in a dynamic network due to the topological evolution over time, known as actor dynamicities. Although there has been some success in developing various methodologies and metrics for the purpose of dynamic link prediction, mining actor-oriented evolutions to address this problem has received little attention from the research community. In addition to this, the existing methodologies were developed without considering the sampling window size of the dynamic network, even though the sampling duration has a large impact on mining the network dynamics of an evolutionary network. Therefore, although the principal focus of this thesis is link prediction in dynamic networks, the optimal sampling window determination was also considered
Robust Knowledge Adaptation for Dynamic Graph Neural Networks
Graph structured data often possess dynamic characters in nature, e.g., the
addition of links and nodes, in many real-world applications. Recent years have
witnessed the increasing attentions paid to dynamic graph neural networks for
modelling such graph data, where almost all the existing approaches assume that
when a new link is built, the embeddings of the neighbor nodes should be
updated by learning the temporal dynamics to propagate new information.
However, such approaches suffer from the limitation that if the node introduced
by a new connection contains noisy information, propagating its knowledge to
other nodes is not reliable and even leads to the collapse of the model. In
this paper, we propose AdaNet: a robust knowledge Adaptation framework via
reinforcement learning for dynamic graph neural Networks. In contrast to
previous approaches immediately updating the embeddings of the neighbor nodes
once adding a new link, AdaNet attempts to adaptively determine which nodes
should be updated because of the new link involved. Considering that the
decision whether to update the embedding of one neighbor node will have great
impact on other neighbor nodes, we thus formulate the selection of node update
as a sequence decision problem, and address this problem via reinforcement
learning. By this means, we can adaptively propagate knowledge to other nodes
for learning robust node embedding representations. To the best of our
knowledge, our approach constitutes the first attempt to explore robust
knowledge adaptation via reinforcement learning for dynamic graph neural
networks. Extensive experiments on three benchmark datasets demonstrate that
AdaNet achieves the state-of-the-art performance. In addition, we perform the
experiments by adding different degrees of noise into the dataset,
quantitatively and qualitatively illustrating the robustness of AdaNet.Comment: 14 pages, 6 figure
A Dynamic Embedding Model of the Media Landscape
Information about world events is disseminated through a wide variety of news
channels, each with specific considerations in the choice of their reporting.
Although the multiplicity of these outlets should ensure a variety of
viewpoints, recent reports suggest that the rising concentration of media
ownership may void this assumption. This observation motivates the study of the
impact of ownership on the global media landscape and its influence on the
coverage the actual viewer receives. To this end, the selection of reported
events has been shown to be informative about the high-level structure of the
news ecosystem. However, existing methods only provide a static view into an
inherently dynamic system, providing underperforming statistical models and
hindering our understanding of the media landscape as a whole.
In this work, we present a dynamic embedding method that learns to capture
the decision process of individual news sources in their selection of reported
events while also enabling the systematic detection of large-scale
transformations in the media landscape over prolonged periods of time. In an
experiment covering over 580M real-world event mentions, we show our approach
to outperform static embedding methods in predictive terms. We demonstrate the
potential of the method for news monitoring applications and investigative
journalism by shedding light on important changes in programming induced by
mergers and acquisitions, policy changes, or network-wide content diffusion.
These findings offer evidence of strong content convergence trends inside large
broadcasting groups, influencing the news ecosystem in a time of increasing
media ownership concentration
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey
Dynamic networks are used in a wide range of fields, including social network
analysis, recommender systems, and epidemiology. Representing complex networks
as structures changing over time allow network models to leverage not only
structural but also temporal patterns. However, as dynamic network literature
stems from diverse fields and makes use of inconsistent terminology, it is
challenging to navigate. Meanwhile, graph neural networks (GNNs) have gained a
lot of attention in recent years for their ability to perform well on a range
of network science tasks, such as link prediction and node classification.
Despite the popularity of graph neural networks and the proven benefits of
dynamic network models, there has been little focus on graph neural networks
for dynamic networks. To address the challenges resulting from the fact that
this research crosses diverse fields as well as to survey dynamic graph neural
networks, this work is split into two main parts. First, to address the
ambiguity of the dynamic network terminology we establish a foundation of
dynamic networks with consistent, detailed terminology and notation. Second, we
present a comprehensive survey of dynamic graph neural network models using the
proposed terminologyComment: 28 pages, 9 figures, 8 table
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity
Nowadays, events usually burst and are propagated online through multiple
modern media like social networks and search engines. There exists various
research discussing the event dissemination trends on individual medium, while
few studies focus on event popularity analysis from a cross-platform
perspective. Challenges come from the vast diversity of events and media,
limited access to aligned datasets across different media and a great deal of
noise in the datasets. In this paper, we design DancingLines, an innovative
scheme that captures and quantitatively analyzes event popularity between
pairwise text media. It contains two models: TF-SW, a semantic-aware popularity
quantification model, based on an integrated weight coefficient leveraging
Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series
alignment model matching different event phases adapted from Dynamic Time
Warping. We also propose three metrics to interpret event popularity trends
between pairwise social platforms. Experimental results on eighteen real-world
event datasets from an influential social network and a popular search engine
validate the effectiveness and applicability of our scheme. DancingLines is
demonstrated to possess broad application potentials for discovering the
knowledge of various aspects related to events and different media
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