Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …

Evolutionary network analysis has found an increasing interest in the literature because of
the importance of different kinds of dynamic social networks, email networks, biological …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly developing branch of learning on structured data. During the past 20 years, the …

With the rapid development of information storage and networking technologies, quintillion
bytes of data are generated every day from social networks, business transactions, sensors …

Most state-of-the-art graph kernels only take local graph properties into account, ie, the
kernel is computed with regard to properties of the neighborhood of vertices or other small …

Embeddings have become a key paradigm to learn graph representations and facilitate
downstream graph analysis tasks. Existing graph embedding techniques either sample a …

Networks are ubiquitous in the real world. Link prediction, as one of the key problems for
network-structured data, aims to predict whether there exists a link between two nodes. The …

Many applications involve stream data with structural dependency, graph representations,
and continuously increasing volumes. For these applications, it is very common that their …

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network embedding aims to learn a low-dimensional representation for each node of a …

Histogram-based similarity has been widely adopted in many machine learning tasks.
However, measuring histogram similarity is a challenging task for streaming data, where the …