
DistancePreserving Graph Embeddings from Random Neural Features
We present Graph Random Neural Features (GRNF), a novel embedding method...
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Autoregressive Models for Sequences of Graphs
This paper proposes an autoregressive (AR) model for sequences of graphs...
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Change Point Methods on a Sequence of Graphs
The present paper considers a finite sequence of graphs, e.g., coming fr...
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Learning Graph Embeddings on ConstantCurvature Manifolds for Change Detection in Graph Streams
The space of graphs is characterized by a nontrivial geometry, which of...
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Anomaly and Change Detection in Graph Streams through ConstantCurvature Manifold Embeddings
Mapping complex input data into suitable lower dimensional manifolds is ...
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Concept Drift and Anomaly Detection in Graph Streams
Graph representations offer powerful and intuitive ways to describe data...
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Daniele Zambon
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