224 research outputs found
Semantic Neighborhoods as Hypergraphs
Ambiguity preserving representations such as lattices are very useful in a number of NLP tasks, including paraphrase generation, paraphrase recognition, and machine translation evaluation. Lattices compactly represent lexical variation, but word order variation leads to a combinatorial explosion of states. We advocate hypergraphs as compact representations for sets of utterances describing the same event or object. We present a method to construct hypergraphs from sets of utterances, and evaluate this method on a simple recognition task. Given a set of utterances that describe a single object or event, we construct such a hypergraph, and demonstrate that it can recognize novel descriptions of the same event with high accuracy
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Structural Deep Embedding for Hyper-Networks
Network embedding has recently attracted lots of attentions in data mining.
Existing network embedding methods mainly focus on networks with pairwise
relationships. In real world, however, the relationships among data points
could go beyond pairwise, i.e., three or more objects are involved in each
relationship represented by a hyperedge, thus forming hyper-networks. These
hyper-networks pose great challenges to existing network embedding methods when
the hyperedges are indecomposable, that is to say, any subset of nodes in a
hyperedge cannot form another hyperedge. These indecomposable hyperedges are
especially common in heterogeneous networks. In this paper, we propose a novel
Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with
indecomposable hyperedges. More specifically, we theoretically prove that any
linear similarity metric in embedding space commonly used in existing methods
cannot maintain the indecomposibility property in hyper-networks, and thus
propose a new deep model to realize a non-linear tuplewise similarity function
while preserving both local and global proximities in the formed embedding
space. We conduct extensive experiments on four different types of
hyper-networks, including a GPS network, an online social network, a drug
network and a semantic network. The empirical results demonstrate that our
method can significantly and consistently outperform the state-of-the-art
algorithms.Comment: Accepted by AAAI 1
Towards hypergraph cognitive networks as feature-rich models of knowledge
Semantic networks provide a useful tool to understand how related concepts
are retrieved from memory. However, most current network approaches use
pairwise links to represent memory recall patterns. Pairwise connections
neglect higher-order associations, i.e. relationships between more than two
concepts at a time. These higher-order interactions might covariate with (and
thus contain information about) how similar concepts are along psycholinguistic
dimensions like arousal, valence, familiarity, gender and others. We overcome
these limits by introducing feature-rich cognitive hypergraphs as quantitative
models of human memory where: (i) concepts recalled together can all engage in
hyperlinks involving also more than two concepts at once (cognitive hypergraph
aspect), and (ii) each concept is endowed with a vector of psycholinguistic
features (feature-rich aspect). We build hypergraphs from word association data
and use evaluation methods from machine learning features to predict concept
concreteness. Since concepts with similar concreteness tend to cluster together
in human memory, we expect to be able to leverage this structure. Using word
association data from the Small World of Words dataset, we compared a pairwise
network and a hypergraph with N=3586 concepts/nodes. Interpretable artificial
intelligence models trained on (1) psycholinguistic features only, (2)
pairwise-based feature aggregations, and on (3) hypergraph-based aggregations
show significant differences between pairwise and hypergraph links.
Specifically, our results show that higher-order and feature-rich hypergraph
models contain richer information than pairwise networks leading to improved
prediction of word concreteness. The relation with previous studies about
conceptual clustering and compartmentalisation in associative knowledge and
human memory are discussed
Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions
Recent advances in network science have resulted in two distinct research
directions aimed at augmenting and enhancing representations for complex
networks. The first direction, that of high-order modeling, aims to focus on
connectivity between sets of nodes rather than pairs, whereas the second one,
that of feature-rich augmentation, incorporates into a network all those
elements that are driven by information which is external to the structure,
like node properties or the flow of time. This paper proposes a novel toolbox,
that of Attributed Stream Hypergraphs (ASHs), unifying both high-order and
feature-rich elements for representing, mining, and analyzing complex networks.
Applied to social network analysis, ASHs can characterize complex social
phenomena along topological, dynamic and attributive elements. Experiments on
real-world face-to-face and online social media interactions highlight that
ASHs can easily allow for the analyses, among others, of high-order groups'
homophily, nodes' homophily with respect to the hyperedges in which nodes
participate, and time-respecting paths between hyperedges.Comment: Submitted to "Applied Network Science
Architectures of Topological Deep Learning: A Survey on Topological Neural Networks
The natural world is full of complex systems characterized by intricate
relations between their components: from social interactions between
individuals in a social network to electrostatic interactions between atoms in
a protein. Topological Deep Learning (TDL) provides a comprehensive framework
to process and extract knowledge from data associated with these systems, such
as predicting the social community to which an individual belongs or predicting
whether a protein can be a reasonable target for drug development. TDL has
demonstrated theoretical and practical advantages that hold the promise of
breaking ground in the applied sciences and beyond. However, the rapid growth
of the TDL literature has also led to a lack of unification in notation and
language across Topological Neural Network (TNN) architectures. This presents a
real obstacle for building upon existing works and for deploying TNNs to new
real-world problems. To address this issue, we provide an accessible
introduction to TDL, and compare the recently published TNNs using a unified
mathematical and graphical notation. Through an intuitive and critical review
of the emerging field of TDL, we extract valuable insights into current
challenges and exciting opportunities for future development
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