408,881 research outputs found
Multi-Scale Link Prediction
The automated analysis of social networks has become an important problem due
to the proliferation of social networks, such as LiveJournal, Flickr and
Facebook. The scale of these social networks is massive and continues to grow
rapidly. An important problem in social network analysis is proximity
estimation that infers the closeness of different users. Link prediction, in
turn, is an important application of proximity estimation. However, many
methods for computing proximity measures have high computational complexity and
are thus prohibitive for large-scale link prediction problems. One way to
address this problem is to estimate proximity measures via low-rank
approximation. However, a single low-rank approximation may not be sufficient
to represent the behavior of the entire network. In this paper, we propose
Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can
handle massive networks. The basis idea of MSLP is to construct low rank
approximations of the network at multiple scales in an efficient manner. Based
on this approach, MSLP combines predictions at multiple scales to make robust
and accurate predictions. Experimental results on real-life datasets with more
than a million nodes show the superior performance and scalability of our
method.Comment: 20 pages, 10 figure
Convolutional 2D Knowledge Graph Embeddings
Link prediction for knowledge graphs is the task of predicting missing
relationships between entities. Previous work on link prediction has focused on
shallow, fast models which can scale to large knowledge graphs. However, these
models learn less expressive features than deep, multi-layer models -- which
potentially limits performance. In this work, we introduce ConvE, a multi-layer
convolutional network model for link prediction, and report state-of-the-art
results for several established datasets. We also show that the model is highly
parameter efficient, yielding the same performance as DistMult and R-GCN with
8x and 17x fewer parameters. Analysis of our model suggests that it is
particularly effective at modelling nodes with high indegree -- which are
common in highly-connected, complex knowledge graphs such as Freebase and
YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer
from test set leakage, due to inverse relations from the training set being
present in the test set -- however, the extent of this issue has so far not
been quantified. We find this problem to be severe: a simple rule-based model
can achieve state-of-the-art results on both WN18 and FB15k. To ensure that
models are evaluated on datasets where simply exploiting inverse relations
cannot yield competitive results, we investigate and validate several commonly
used datasets -- deriving robust variants where necessary. We then perform
experiments on these robust datasets for our own and several previously
proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal
Rank across most datasets.Comment: Extended AAAI2018 pape
Simplifying Subgraph Representation Learning for Scalable Link Prediction
Link prediction on graphs is a fundamental problem. Subgraph representation
learning approaches (SGRLs), by transforming link prediction to graph
classification on the subgraphs around the links, have achieved
state-of-the-art performance in link prediction. However, SGRLs are
computationally expensive, and not scalable to large-scale graphs due to
expensive subgraph-level operations. To unlock the scalability of SGRLs, we
propose a new class of SGRLs, that we call Scalable Simplified SGRL (S3GRL).
Aimed at faster training and inference, S3GRL simplifies the message passing
and aggregation operations in each link's subgraph. S3GRL, as a scalability
framework, accommodates various subgraph sampling strategies and diffusion
operators to emulate computationally-expensive SGRLs. We propose multiple
instances of S3GRL and empirically study them on small to large-scale graphs.
Our extensive experiments demonstrate that the proposed S3GRL models scale up
SGRLs without significant performance compromise (even with considerable gains
in some cases), while offering substantially lower computational footprints
(e.g., multi-fold inference and training speedup)
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