2,733 research outputs found
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?
Network embedding methods map a network's nodes to vectors in an embedding
space, in such a way that these representations are useful for estimating some
notion of similarity or proximity between pairs of nodes in the network. The
quality of these node representations is then showcased through results of
downstream prediction tasks. Commonly used benchmark tasks such as link
prediction, however, present complex evaluation pipelines and an abundance of
design choices. This, together with a lack of standardized evaluation setups
can obscure the real progress in the field. In this paper, we aim to shed light
on the state-of-the-art of network embedding methods for link prediction and
show, using a consistent evaluation pipeline, that only thin progress has been
made over the last years. The newly conducted benchmark that we present here,
including 17 embedding methods, also shows that many approaches are
outperformed even by simple heuristics. Finally, we argue that standardized
evaluation tools can repair this situation and boost future progress in this
field
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
Graph Convolutional Networks (GCNs) are extensively utilized for deep
learning on graphs. The large data sizes of graphs and their vertex features
make scalable training algorithms and distributed memory systems necessary.
Since the convolution operation on graphs induces irregular memory access
patterns, designing a memory- and communication-efficient parallel algorithm
for GCN training poses unique challenges. We propose a highly parallel training
algorithm that scales to large processor counts. In our solution, the large
adjacency and vertex-feature matrices are partitioned among processors. We
exploit the vertex-partitioning of the graph to use non-blocking point-to-point
communication operations between processors for better scalability. To further
minimize the parallelization overheads, we introduce a sparse matrix
partitioning scheme based on a hypergraph partitioning model for full-batch
training. We also propose a novel stochastic hypergraph model to encode the
expected communication volume in mini-batch training. We show the merits of the
hypergraph model, previously unexplored for GCN training, over the standard
graph partitioning model which does not accurately encode the communication
costs. Experiments performed on real-world graph datasets demonstrate that the
proposed algorithms achieve considerable speedups over alternative solutions.
The optimizations achieved on communication costs become even more pronounced
at high scalability with many processors. The performance benefits are
preserved in deeper GCNs having more layers as well as on billion-scale graphs.Comment: To appear in PVLDB'2
Fishnets: Information-Optimal, Scalable Aggregation for Sets and Graphs
Set-based learning is an essential component of modern deep learning and
network science. Graph Neural Networks (GNNs) and their edge-free counterparts
Deepsets have proven remarkably useful on ragged and topologically challenging
datasets. The key to learning informative embeddings for set members is a
specified aggregation function, usually a sum, max, or mean. We propose
Fishnets, an aggregation strategy for learning information-optimal embeddings
for sets of data for both Bayesian inference and graph aggregation. We
demonstrate that i) Fishnets neural summaries can be scaled optimally to an
arbitrary number of data objects, ii) Fishnets aggregations are robust to
changes in data distribution, unlike standard deepsets, iii) Fishnets saturate
Bayesian information content and extend to regimes where MCMC techniques fail
and iv) Fishnets can be used as a drop-in aggregation scheme within GNNs. We
show that by adopting a Fishnets aggregation scheme for message passing, GNNs
can achieve state-of-the-art performance versus architecture size on
ogbn-protein data over existing benchmarks with a fraction of learnable
parameters and faster training time.Comment: 13 pages, 9 figures, 2 tables. Submitted to ICLR 202
Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection using Enhanced Neural Networks
Outlier detection has received special attention in various fields, mainly
for those dealing with machine learning and artificial intelligence. As strong
outliers, anomalies are divided into the point, contextual and collective
outliers. The most important challenges in outlier detection include the thin
boundary between the remote points and natural area, the tendency of new data
and noise to mimic the real data, unlabelled datasets and different definitions
for outliers in different applications. Considering the stated challenges, we
defined new types of anomalies called Collective Normal Anomaly and Collective
Point Anomaly in order to improve a much better detection of the thin boundary
between different types of anomalies. Basic domain-independent methods are
introduced to detect these defined anomalies in both unsupervised and
supervised datasets. The Multi-Layer Perceptron Neural Network is enhanced
using the Genetic Algorithm to detect newly defined anomalies with higher
precision so as to ensure a test error less than that calculated for the
conventional Multi-Layer Perceptron Neural Network. Experimental results on
benchmark datasets indicated reduced error of anomaly detection process in
comparison to baselines
Machine Learning-Based Anomaly Detection in Cloud Virtual Machine Resource Usage
Anomaly detection is an important activity in cloud computing systems because it aids in the identification of odd behaviours or actions that may result in software glitch, security breaches, and performance difficulties. Detecting aberrant resource utilization trends in virtual machines is a typical application of anomaly detection in cloud computing (VMs). Currently, the most serious cyber threat is distributed denial-of-service attacks. The afflicted server\u27s resources and internet traffic resources, such as bandwidth and buffer size, are slowed down by restricting the server\u27s capacity to give resources to legitimate customers.
To recognize attacks and common occurrences, machine learning techniques such as Quadratic Support Vector Machines (QSVM), Random Forest, and neural network models such as MLP and Autoencoders are employed. Various machine learning algorithms are used on the optimised NSL-KDD dataset to provide an efficient and accurate predictor of network intrusions. In this research, we propose a neural network based model and experiment on various central and spiral rearrangements of the features for distinguishing between different types of attacks and support our approach of better preservation of feature structure with image representations. The results are analysed and compared to existing models and prior research. The outcomes of this study have practical implications for improving the security and performance of cloud computing systems, specifically in the area of identifying and mitigating network intrusions
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