30,988 research outputs found
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network
The application of deep learning to symbolic domains remains an active
research endeavour. Graph neural networks (GNN), consisting of trained neural
modules which can be arranged in different topologies at run time, are sound
alternatives to tackle relational problems which lend themselves to graph
representations. In this paper, we show that GNNs are capable of multitask
learning, which can be naturally enforced by training the model to refine a
single set of multidimensional embeddings and decode them
into multiple outputs by connecting MLPs at the end of the pipeline. We
demonstrate the multitask learning capability of the model in the relevant
relational problem of estimating network centrality measures, focusing
primarily on producing rankings based on these measures, i.e. is vertex
more central than vertex given centrality ?. We then show that a GNN
can be trained to develop a \emph{lingua franca} of vertex embeddings from
which all relevant information about any of the trained centrality measures can
be decoded. The proposed model achieves accuracy on a test dataset of
random instances with up to 128 vertices and is shown to generalise to larger
problem sizes. The model is also shown to obtain reasonable accuracy on a
dataset of real world instances with up to 4k vertices, vastly surpassing the
sizes of the largest instances with which the model was trained ().
Finally, we believe that our contributions attest to the potential of GNNs in
symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure
Asynchronous Graph Pattern Matching on Multiprocessor Systems
Pattern matching on large graphs is the foundation for a variety of
application domains. Strict latency requirements and continuously increasing
graph sizes demand the usage of highly parallel in-memory graph processing
engines that need to consider non-uniform memory access (NUMA) and concurrency
issues to scale up on modern multiprocessor systems. To tackle these aspects,
graph partitioning becomes increasingly important. Hence, we present a
technique to process graph pattern matching on NUMA systems in this paper. As a
scalable pattern matching processing infrastructure, we leverage a
data-oriented architecture that preserves data locality and minimizes
concurrency-related bottlenecks on NUMA systems. We show in detail, how graph
pattern matching can be asynchronously processed on a multiprocessor system.Comment: 14 Pages, Extended version for ADBIS 201
Parallel Processing of Large Graphs
More and more large data collections are gathered worldwide in various IT
systems. Many of them possess the networked nature and need to be processed and
analysed as graph structures. Due to their size they require very often usage
of parallel paradigm for efficient computation. Three parallel techniques have
been compared in the paper: MapReduce, its map-side join extension and Bulk
Synchronous Parallel (BSP). They are implemented for two different graph
problems: calculation of single source shortest paths (SSSP) and collective
classification of graph nodes by means of relational influence propagation
(RIP). The methods and algorithms are applied to several network datasets
differing in size and structural profile, originating from three domains:
telecommunication, multimedia and microblog. The results revealed that
iterative graph processing with the BSP implementation always and
significantly, even up to 10 times outperforms MapReduce, especially for
algorithms with many iterations and sparse communication. Also MapReduce
extension based on map-side join usually noticeably presents better efficiency,
although not as much as BSP. Nevertheless, MapReduce still remains the good
alternative for enormous networks, whose data structures do not fit in local
memories.Comment: Preprint submitted to Future Generation Computer System
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