4,366 research outputs found
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization
We propose a scalable temporal latent space model for link prediction in
dynamic social networks, where the goal is to predict links over time based on
a sequence of previous graph snapshots. The model assumes that each user lies
in an unobserved latent space and interactions are more likely to form between
similar users in the latent space representation. In addition, the model allows
each user to gradually move its position in the latent space as the network
structure evolves over time. We present a global optimization algorithm to
effectively infer the temporal latent space, with a quadratic convergence rate.
Two alternative optimization algorithms with local and incremental updates are
also proposed, allowing the model to scale to larger networks without
compromising prediction accuracy. Empirically, we demonstrate that our model,
when evaluated on a number of real-world dynamic networks, significantly
outperforms existing approaches for temporal link prediction in terms of both
scalability and predictive power.Comment: Technical report for paper "Scalable Temporal Latent Space Inference
for Link Prediction in Dynamic Social Networks" that appears in IEEE
Transactions on Knowledge and Data Engineering 201
Community Specific Temporal Topic Discovery from Social Media
Studying temporal dynamics of topics in social media is very useful to
understand online user behaviors. Most of the existing work on this subject
usually monitors the global trends, ignoring variation among communities. Since
users from different communities tend to have varying tastes and interests,
capturing community-level temporal change can improve the understanding and
management of social content. Additionally, it can further facilitate the
applications such as community discovery, temporal prediction and online
marketing. However, this kind of extraction becomes challenging due to the
intricate interactions between community and topic, and intractable
computational complexity.
In this paper, we take a unified solution towards the community-level topic
dynamic extraction. A probabilistic model, CosTot (Community Specific
Topics-over-Time) is proposed to uncover the hidden topics and communities, as
well as capture community-specific temporal dynamics. Specifically, CosTot
considers text, time, and network information simultaneously, and well
discovers the interactions between community and topic over time. We then
discuss the approximate inference implementation to enable scalable computation
of model parameters, especially for large social data. Based on this, the
application layer support for multi-scale temporal analysis and community
exploration is also investigated.
We conduct extensive experimental studies on a large real microblog dataset,
and demonstrate the superiority of proposed model on tasks of time stamp
prediction, link prediction and topic perplexity.Comment: 12 pages, 16 figures, submitted to VLDB 201
Learning Dynamic Embeddings from Temporal Interactions
Modeling a sequence of interactions between users and items (e.g., products,
posts, or courses) is crucial in domains such as e-commerce, social networking,
and education to predict future interactions. Representation learning presents
an attractive solution to model the dynamic evolution of user and item
properties, where each user/item can be embedded in a euclidean space and its
evolution can be modeled by dynamic changes in embedding. However, existing
embedding methods either generate static embeddings, treat users and items
independently, or are not scalable.
Here we present JODIE, a coupled recurrent model to jointly learn the dynamic
embeddings of users and items from a sequence of user-item interactions. JODIE
has three components. First, the update component updates the user and item
embedding from each interaction using their previous embeddings with the two
mutually-recursive Recurrent Neural Networks. Second, a novel projection
component is trained to forecast the embedding of users at any future time.
Finally, the prediction component directly predicts the embedding of the item
in a future interaction. For models that learn from a sequence of interactions,
traditional training data batching cannot be done due to complex user-user
dependencies. Therefore, we present a novel batching algorithm called t-Batch
that generates time-consistent batches of training data that can run in
parallel, giving massive speed-up.
We conduct six experiments on two prediction tasks---future interaction
prediction and state change prediction---using four real-world datasets. We
show that JODIE outperforms six state-of-the-art algorithms in these tasks by
up to 22.4%. Moreover, we show that JODIE is highly scalable and up to 9.2x
faster than comparable models. As an additional experiment, we illustrate that
JODIE can predict student drop-out from courses five interactions in advance
A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data
As a representation of relational data over time series, longitudinal
networks provide opportunities to study link formation processes. However,
networks at scale often exhibits community structure (i.e. clustering), which
may confound local structural effects if it is not considered appropriately in
statistical analysis. To infer the (possibly) evolving clusters and other
network structures (e.g. degree distribution and/or transitivity) within each
community, simultaneously, we propose a class of statistical models named
Temporal Hierarchical Exponential Random Graph Models (THERGM). Our generative
model imposes a Markovian transition matrix for nodes to change their
membership, and assumes they join new community in a preferential attachment
way. For those remaining in the same cluster, they follow a specific temporal
ERG model (TERGM). While a direct MCMC based Bayesian estimation is
computational infeasible, we propose a two-stage strategy. At the first stage,
a specific dynamic latent space model will be used as the working model for
clustering. At the second stage, estimated memberships are taken as given to
fit a TERG model in each cluster. We evaluate our methods on simulated data in
terms of the mis-clustering rate, as well as the goodness of fit and link
prediction accuracy
Sequential Edge Clustering in Temporal Multigraphs
Interaction graphs, such as those recording emails between individuals or
transactions between institutions, tend to be sparse yet structured, and often
grow in an unbounded manner. Such behavior can be well-captured by structured,
nonparametric edge-exchangeable graphs. However, such exchangeable models
necessarily ignore temporal dynamics in the network. We propose a dynamic
nonparametric model for interaction graphs that combine the sparsity of the
exchangeable models with dynamic clustering patterns that tend to reinforce
recent behavioral patterns. We show that our method yields improved held-out
likelihood over stationary variants, and impressive predictive performance
against a range of state-of-the-art dynamic interaction graph models
Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes
In a dynamic network, the neighborhood of the vertices evolve across
different temporal snapshots of the network. Accurate modeling of this temporal
evolution can help solve complex tasks involving real-life social and
interaction networks. However, existing models for learning latent
representation are inadequate for obtaining the representation vectors of the
vertices for different time-stamps of a dynamic network in a meaningful way. In
this paper, we propose latent representation learning models for dynamic
networks which overcome the above limitation by considering two different kinds
of temporal smoothness: (i) retrofitted, and (ii) linear transformation. The
retrofitted model tracks the representation vector of a vertex over time,
facilitating vertex-based temporal analysis of a network. On the other hand,
linear transformation based model provides a smooth transition operator which
maps the representation vectors of all vertices from one temporal snapshot to
the next (unobserved) snapshot-this facilitates prediction of the state of a
network in a future time-stamp. We validate the performance of our proposed
models by employing them for solving the temporal link prediction task.
Experiments on 9 real-life networks from various domains validate that the
proposed models are significantly better than the existing models for
predicting the dynamics of an evolving network
Link Prediction in Social Networks: the State-of-the-Art
In social networks, link prediction predicts missing links in current
networks and new or dissolution links in future networks, is important for
mining and analyzing the evolution of social networks. In the past decade, many
works have been done about the link prediction in social networks. The goal of
this paper is to comprehensively review, analyze and discuss the
state-of-the-art of the link prediction in social networks. A systematical
category for link prediction techniques and problems is presented. Then link
prediction techniques and problems are analyzed and discussed. Typical
applications of link prediction are also addressed. Achievements and roadmaps
of some active research groups are introduced. Finally, some future challenges
of the link prediction in social networks are discussed.Comment: 38 pages, 13 figures, Science China: Information Science, 201
Unifying Local and Global Change Detection in Dynamic Networks
Many real-world networks are complex dynamical systems, where both local
(e.g., changing node attributes) and global (e.g., changing network topology)
processes unfold over time. Local dynamics may provoke global changes in the
network, and the ability to detect such effects could have profound
implications for a number of real-world problems. Most existing techniques
focus individually on either local or global aspects of the problem or treat
the two in isolation from each other. In this paper we propose a novel network
model that simultaneously accounts for both local and global dynamics. To the
best of our knowledge, this is the first attempt at modeling and detecting
local and global change points on dynamic networks via a unified generative
framework. Our model is built upon the popular mixed membership stochastic
blockmodels (MMSB) with sparse co-evolving patterns. We derive an efficient
stochastic gradient Langevin dynamics (SGLD) sampler for our proposed model,
which allows it to scale to potentially very large networks. Finally, we
validate our model on both synthetic and real-world data and demonstrate its
superiority over several baselines
A Survey of Heterogeneous Information Network Analysis
Most real systems consist of a large number of interacting, multi-typed
components, while most contemporary researches model them as homogeneous
networks, without distinguishing different types of objects and links in the
networks. Recently, more and more researchers begin to consider these
interconnected, multi-typed data as heterogeneous information networks, and
develop structural analysis approaches by leveraging the rich semantic meaning
of structural types of objects and links in the networks. Compared to widely
studied homogeneous network, the heterogeneous information network contains
richer structure and semantic information, which provides plenty of
opportunities as well as a lot of challenges for data mining. In this paper, we
provide a survey of heterogeneous information network analysis. We will
introduce basic concepts of heterogeneous information network analysis, examine
its developments on different data mining tasks, discuss some advanced topics,
and point out some future research directions.Comment: 45 pages, 12 figure
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction
Predicting the potential relations between nodes in networks, known as link
prediction, has long been a challenge in network science. However, most studies
just focused on link prediction of static network, while real-world networks
always evolve over time with the occurrence and vanishing of nodes and links.
Dynamic network link prediction thus has been attracting more and more
attention since it can better capture the evolution nature of networks, but
still most algorithms fail to achieve satisfied prediction accuracy. Motivated
by the excellent performance of Long Short-Term Memory (LSTM) in processing
time series, in this paper, we propose a novel Encoder-LSTM-Decoder (E-LSTM-D)
deep learning model to predict dynamic links end to end. It could handle long
term prediction problems, and suits the networks of different scales with
fine-tuned structure. To the best of our knowledge, it is the first time that
LSTM, together with an encoder-decoder architecture, is applied to link
prediction in dynamic networks. This new model is able to automatically learn
structural and temporal features in a unified framework, which can predict the
links that never appear in the network before. The extensive experiments show
that our E-LSTM-D model significantly outperforms newly proposed dynamic
network link prediction methods and obtain the state-of-the-art results.Comment: 12 pages, 6 figure
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