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In this work, we present a method for node embedding in temporal graphs.
The procrustes program: Producing direct rotation to test a hypothesized factor structure
John R Hurley and Raymond B Cattell · 1962
Earlier work this paper cites.
Spectra of some self-exciting and mutually exciting point processes
Alan G Hawkes · 1971
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Social network analysis: Methods and applications
Stanley Wasserman and Katherine Faust · 1994
Earlier work this paper cites.
Emergence of scaling in random networks
Albert-Laszlo Barabasi and Reka Albert · 1999
Earlier work this paper cites.
Graphs over time: Densification laws, shrinking diameters and possible explanations
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Ashwini Patil and Haruki Nakamura · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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