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Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects.
First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work t ...
A major line of work in graph signal processing [2] during the past 10 years has been to design new transform methods that account for the underlying graph structure to identify and exploit structure in data residing on a connected, weighted, undirected gr ...
Effective information retrieval (IR) relies on the ability to comprehensively capture a user's information needs. Traditional IR systems are limited to homogeneous queries that define the information to retrieve by a single modality. Support for heterogene ...
Spectral Graph Convolutional Networks (GCNs) are generalisations of standard convolutional for graph-structured data using the Laplacian operator. Recent work has shown that spectral GCNs have an intrinsic transferability. This work verifies this by studyi ...
We propose the Recursive Non-autoregressive Graph-to-graph Transformer architecture (RNG-Tr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dep ...
We confirm, for the primes up to 3000, the conjecture of Bourgain-Gamburd-Sarnak and Baragar on strong approximation for the Markoff surface modulo primes. For primes congruent to 3 modulo 4, we find data suggesting that some natural graphs constructed fro ...
The current information landscape is characterised by a vast amount of relatively semantically homogeneous, when observed in isolation, data silos that are, however, drastically semantically fragmented when considered as a whole. Within each data silo, inf ...
Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do n ...
The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, d ...
In graph coarsening, one aims to produce a coarse graph of reduced size while preserving important graph properties. However, as there is no consensus on which specific graph properties should be preserved by coarse graphs, measuring the differences betwee ...