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However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and predictive modeling tasks. In this work, we propose a general framework for temporal network sampling with unbiased estimation. We develop online, single-pass sampling algorithms, and unbiased estimators for temporal network sampling. The proposed algorithms enable fast, accurate, and memory-efficient statistical estimation of temporal network patterns and properties. In addition, we propose a temporally decaying sampling algorithm with unbiased estimators for studying networks that evolve in continuous time, where the strength of links is a function of time, and the motif patterns are temporally weighted. In contrast to the prior notion of a \u25b3\n            <jats:italic>t<\/jats:italic>\n            -temporal motif, the proposed formulation and algorithms for counting temporally weighted motifs are useful for forecasting tasks in networks such as predicting future links, or a future time-series variable of nodes and links. Finally, extensive experiments on a variety of temporal networks from different domains demonstrate the effectiveness of the proposed algorithms. A detailed ablation study is provided to understand the impact of the various components of the proposed framework.\n          <\/jats:p>","DOI":"10.1145\/3442202","type":"journal-article","created":{"date-parts":[[2021,4,18]],"date-time":"2021-04-18T16:05:45Z","timestamp":1618761945000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Online Sampling of Temporal Networks"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7913-4962","authenticated-orcid":false,"given":"Nesreen K.","family":"Ahmed","sequence":"first","affiliation":[{"name":"Texas A&amp;M University, Santa Clara, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nick","family":"Duffield","sequence":"additional","affiliation":[{"name":"Texas A&amp;M University, Santa Clara, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryan A.","family":"Rossi","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems. 1565--1573","author":"Achlioptas Dimitris","year":"2013","unstructured":"Dimitris Achlioptas , Zohar S. 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