5,404 research outputs found
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of
vertices in a network. These latent representations encode social relations in
a continuous vector space, which is easily exploited by statistical models.
DeepWalk generalizes recent advancements in language modeling and unsupervised
feature learning (or deep learning) from sequences of words to graphs. DeepWalk
uses local information obtained from truncated random walks to learn latent
representations by treating walks as the equivalent of sentences. We
demonstrate DeepWalk's latent representations on several multi-label network
classification tasks for social networks such as BlogCatalog, Flickr, and
YouTube. Our results show that DeepWalk outperforms challenging baselines which
are allowed a global view of the network, especially in the presence of missing
information. DeepWalk's representations can provide scores up to 10%
higher than competing methods when labeled data is sparse. In some experiments,
DeepWalk's representations are able to outperform all baseline methods while
using 60% less training data. DeepWalk is also scalable. It is an online
learning algorithm which builds useful incremental results, and is trivially
parallelizable. These qualities make it suitable for a broad class of real
world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table
Extended Edgecluster based Technique for Social Networking Collective Behavior Learning System
Growing interest and continuous development of social network sites like Facebook Twitter Flicker and YouTube etc turn to several researchers for research study planning and rigorous development Exact people behavior prediction is the most important challenge of these on-line social networking websites This research focus to learn to predict collective behavior in social media networks Particularly provided information about some person how can we collect the behavior of unobserved persons in the same network These tremendous growing networks in social media are of massive size involving large number of actors The computational scale of these networks makes necessary scalable learning for models for collective collaborative behavior prediction This scalability issue is solved by the proposed k-means clustering algorithm which is used to partition the edges into disjoint distinct sets with each set is showing one separate affiliation This edge-centric structure represents that the extracted social dimensions are definitely sparse in nature This model idealized on the sparse natured social dimensions shows efficient prediction performance than earlier existing approaches The proposed approach can effectively able to work for sparse social networks of any growing size The important advantage of this method is that it easily grows upon to handle networks with large number of actors while existing methods was unable to do This scalable approach effectively used over of online network collective behavior on a large scal
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
On electronic game platforms, different payment transactions have different
levels of risk. Risk is generally higher for digital goods in e-commerce.
However, it differs based on product and its popularity, the offer type
(packaged game, virtual currency to a game or subscription service), storefront
and geography. Existing fraud policies and models make decisions independently
for each transaction based on transaction attributes, payment velocities, user
characteristics, and other relevant information. However, suspicious
transactions may still evade detection and hence we propose a broad learning
approach leveraging a graph based perspective to uncover relationships among
suspicious transactions, i.e., inter-transaction dependency. Our focus is to
detect suspicious transactions by capturing common fraudulent behaviors that
would not be considered suspicious when being considered in isolation. In this
paper, we present HitFraud that leverages heterogeneous information networks
for collective fraud detection by exploring correlated and fast evolving
fraudulent behaviors. First, a heterogeneous information network is designed to
link entities of interest in the transaction database via different semantics.
Then, graph based features are efficiently discovered from the network
exploiting the concept of meta-paths, and decisions on frauds are made
collectively on test instances. Experiments on real-world payment transaction
data from Electronic Arts demonstrate that the prediction performance is
effectively boosted by HitFraud with fast convergence where the computation of
meta-path based features is largely optimized. Notably, recall can be improved
up to 7.93% and F-score 4.62% compared to baselines.Comment: ICDM 201
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for low-rank factorization of massive
tensors with binary observations. The proposed model has the following key
properties: (1) in contrast to the models based on the logistic or probit
likelihood, using a zero-truncated Poisson likelihood for binary data allows
our model to scale up in the number of \emph{ones} in the tensor, which is
especially appealing for massive but sparse binary tensors; (2)
side-information in form of binary pairwise relationships (e.g., an adjacency
network) between objects in any tensor mode can also be leveraged, which can be
especially useful in "cold-start" settings; and (3) the model admits simple
Bayesian inference via batch, as well as \emph{online} MCMC; the latter allows
scaling up even for \emph{dense} binary data (i.e., when the number of ones in
the tensor/network is also massive). In addition, non-negative factor matrices
in our model provide easy interpretability, and the tensor rank can be inferred
from the data. We evaluate our model on several large-scale real-world binary
tensors, achieving excellent computational scalability, and also demonstrate
its usefulness in leveraging side-information provided in form of
mode-network(s).Comment: UAI (Uncertainty in Artificial Intelligence) 201
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has
aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Historically, cross-domain co-clustering methods have successfully leveraged
shared users and items across dense and sparse domains to improve inference
quality. However, they rely on shared rating data and cannot scale to multiple
sparse target domains (i.e., the one-to-many transfer setting). This, combined
with the increasing adoption of neural recommender architectures, motivates us
to develop scalable neural layer-transfer approaches for cross-domain learning.
Our key intuition is to guide neural collaborative filtering with
domain-invariant components shared across the dense and sparse domains,
improving the user and item representations learned in the sparse domains. We
leverage contextual invariances across domains to develop these shared modules,
and demonstrate that with user-item interaction context, we can learn-to-learn
informative representation spaces even with sparse interaction data. We show
the effectiveness and scalability of our approach on two public datasets and a
massive transaction dataset from Visa, a global payments technology company
(19% Item Recall, 3x faster vs. training separate models for each domain). Our
approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202
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