Multi-modal Network Representation Learning

dc.conference.date2020-08-23 to 2020-08-27
dc.conference.locationVirtual, Online, USA
dc.conference.name26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
dc.contributor.authorZhang, Chuxu
dc.contributor.authorJiang, Meng
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorYe, Yanfang
dc.contributor.authorChawla, Nitesh V.
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMachine Intelligence & kNowledge Engineering Lab
dc.contributor.institutionBrandeis University, Waltham, MA, USA
dc.contributor.institutionUniversity of Notre Dame, Notre Dame, IN, USA
dc.contributor.institutionCase Western Reserve University, Cleveland, OH, USA
dc.date.accessioned2020-09-17T13:22:02Z
dc.date.available2020-09-17T13:22:02Z
dc.date.issued2020-08-20
dc.date.published-online2020-08-20
dc.date.published-print2020-08-23
dc.description.abstractIn today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context, or their combinations. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space when doing feature engineering for building customized intelligent solutions in response to different purposes. Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications. In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications. These methods will be categorized and introduced in the perspectives of unsupervised, semi-supervised and supervised learning, with corresponding real applications respectively. In the end, we conclude the tutorial and raise open discussions. The authors of this tutorial are active and productive researchers in this area.
dc.eprint.versionPost-print
dc.identifier.citationZhang, C., Jiang, M., Zhang, X., Ye, Y., & Chawla, N. V. (2020). Multi-modal Network Representation Learning. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3394486.3406475
dc.identifier.doi10.1145/3394486.3406475
dc.identifier.eid2-s2.0-85090407277
dc.identifier.isbn9781450379984
dc.identifier.pages3557-3558
dc.identifier.urihttp://hdl.handle.net/10754/665223
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttps://dl.acm.org/doi/10.1145/3394486.3406475
dc.rightsArchived with thanks to ACM
dc.titleMulti-modal Network Representation Learning
dc.typeConference Paper
display.details.left<span><h5>Type</h5>Conference Paper<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Zhang, Chuxu,equals">Zhang, Chuxu</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Jiang, Meng,equals">Jiang, Meng</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0002-3574-5665&spc.sf=dc.date.issued&spc.sd=DESC">Zhang, Xiangliang</a> <a href="https://orcid.org/0000-0002-3574-5665" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Ye, Yanfang,equals">Ye, Yanfang</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Chawla, Nitesh V.,equals">Chawla, Nitesh V.</a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computer Science Program,equals">Computer Science Program</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division,equals">Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Machine Intelligence & kNowledge Engineering Lab,equals">Machine Intelligence & kNowledge Engineering Lab</a><br><br><h5>Online Publication Date</h5>2020-08-20<br><br><h5>Print Publication Date</h5>2020-08-23<br><br><h5>Date</h5>2020-08-20</span>
display.details.right<span><h5>Abstract</h5>In today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context, or their combinations. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space when doing feature engineering for building customized intelligent solutions in response to different purposes. Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications. In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications. These methods will be categorized and introduced in the perspectives of unsupervised, semi-supervised and supervised learning, with corresponding real applications respectively. In the end, we conclude the tutorial and raise open discussions. The authors of this tutorial are active and productive researchers in this area.<br><br><h5>Citation</h5>Zhang, C., Jiang, M., Zhang, X., Ye, Y., & Chawla, N. V. (2020). Multi-modal Network Representation Learning. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. doi:10.1145/3394486.3406475<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Association for Computing Machinery (ACM),equals">Association for Computing Machinery (ACM)</a><br><br><h5>Conference/Event Name</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.conference=26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020,equals">26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1145/3394486.3406475">10.1145/3394486.3406475</a><br><br><h5>Additional Links</h5>https://dl.acm.org/doi/10.1145/3394486.3406475</span>
kaust.personZhang, Xiangliang
orcid.authorZhang, Chuxu
orcid.authorJiang, Meng
orcid.authorZhang, Xiangliang::0000-0002-3574-5665
orcid.authorYe, Yanfang
orcid.authorChawla, Nitesh V.
orcid.id0000-0002-3574-5665
refterms.dateFOA2020-09-17T13:23:39Z
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