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Schedule
http://www.mlgworkshop.org/2020/#program
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Main Workshop Zoom
https://zoom.us/j/97352101734?pwd=dFVmaE5yamY2VmR0Qk01eWNncDF4QT09
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MLG Contributed Talks / Breakout
https://zoom.us/j/92946882275?pwd=SXJPVi9wTENtVGkyb3BraEZYZCthQT09
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Posters Video Playlist
https://www.youtube.com/playlist?list=PLNJJrghmwDuur4R-xOUW203_o-egylH8k
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Morning (Starting 11:30 AM PT)Afternoon (Starting 4:00 PM PT)
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Session 1Session 1
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Adversarial Learning for Debiasing Knowledge Graph Embeddingshttps://www.youtube.com/watch?v=a5fFOKc5os8Join Zoom MeetingExamining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkshttps://www.youtube.com/watch?v=phYf15A5A50
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CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embeddinghttps://www.youtube.com/watch?v=27I2fCRSWu0
https://zoom.us/j/95176781311?pwd=cVVVek1kUXp6VTc0WFY0NUcvUVV2UT09
Substitution Techniques for Grocery Fulfillment and Assortment Optimization Using Product Graphshttps://www.youtube.com/watch?v=NdxX6Djfddw
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Decoupled Smoothing in Probabilistic Soft Logichttps://www.youtube.com/watch?v=icXAvFtF_dETIES: Temporal Interaction Embeddings for Enhancing Social Media Integrity at Facebookhttps://www.youtube.com/watch?v=ZI7flmqxV20
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Understanding and Evaluating Structural Node Embeddingshttps://www.youtube.com/watch?v=Y-YKgsETx3AGraph Frequency Analysis of COVID-19 Incidence in the United Stateshttps://www.youtube.com/watch?v=YalBH6WpS3w
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Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximizationhttps://www.youtube.com/watch?v=2Ox0QURMCRA
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Session 2Session 2
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Karate Club: An API Oriented Open-Source Python Framework for Unsupervised Learning on Graphhttps://www.youtube.com/watch?v=t212-ntxu2UJoin Zoom MeetingMIDAS: Microcluster-Based Detector of Anomalies in Edge Streamshttps://www.youtube.com/watch?v=DPmN-uPW8qU
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Little Ball of Fur: A Python Library for Graph Samplinghttps://www.youtube.com/watch?v=5OpjBqlPWMEhttps://notredame.zoom.us/j/94809878862?pwd=bUc5bThEYTJpaVV0eXlxbytPQTc0Zz09On Structural vs Proximity-based Temporal Node Embeddingshttps://www.youtube.com/watch?v=EvQlYmgRt-8
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SNAPSKETCH: Graph Representation Approach for Intrusion Detection in a Streaming Graphhttps://www.youtube.com/watch?v=wi9DISFQXLoLink Predictions in an Online Health Community for Smoking Cessationhttps://www.youtube.com/watch?v=W_nJG5x_fPw
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Learning Distrbuted Representations of Graphs with Geo2DRhttps://www.youtube.com/watch?v=X2AwBcyqtukMeeting ID: 948 0987 8862Network Embedding with Attribute Refinementhttps://www.youtube.com/watch?v=4E9-kVVlggc
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Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative modelshttps://www.youtube.com/watch?v=t3SsWfy6DF0Passcode: 873021Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environmentshttps://www.youtube.com/watch?v=7pedDukqAiU
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Session 3Session 3
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Graph Summarization and Graph Embeddings: A Spectral Connectionhttps://www.youtube.com/watch?v=laFmO6Jn-Q4Join Zoom MeetingFirst- and Higher-Order Bipartite Embeddingshttps://www.youtube.com/watch?v=_lXLnlNDWfI
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Graph-based State Representations for Deep Reinforcement Learninghttps://www.youtube.com/watch?v=n7-XdEl_Ehs
https://fb.zoom.us/j/2334607637
Comparison of Graph Generation Models focusing on Accuracy and Variationhttps://www.youtube.com/watch?v=sP_xFH0b9Oo
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Heterogeneous Threshold Estimation for Linear Threshold Modelinghttps://www.youtube.com/watch?v=3sjCsQkq7hoPasscode: 546879BRGAN: Generating Graphs of Bounded Rankhttps://www.youtube.com/watch?v=PXcd2VKPNbA
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Effectiveness of Sampling Strategies for One-shot Active Learning from Relational Datahttps://www.youtube.com/watch?v=_XeHurAjVOsAdaptive Granularity in Time Evolving Graphs as Tensorshttps://www.youtube.com/watch?v=gKAFQeO4Mtg
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Learning Generic Representation for Dynamic Social Interactionhttps://www.youtube.com/watch?v=i0uxMh0RBrQ
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Collective Bio-entity Recognition via Hinge-Loss Markov Random Fieldshttps://www.youtube.com/watch?v=u1VWK70BlbY
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Session 4
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A Scalable Parallel Hypergraph Generator (HyGen)https://www.youtube.com/watch?v=27I2fCRSWu0
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Active Learning on Graphs with Geodesically Convex Classeshttps://www.youtube.com/watch?v=MsxJT2cP8yg
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Efficient Algorithms to Mine Maximal Span-Trusses From Temporal Graphshttps://www.youtube.com/watch?v=27I2fCRSWu0
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GATCheck: A Detailed Analysis of Graph Attention Networkshttps://www.youtube.com/watch?v=w6ea75UkfP0
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Influence of Asymmetry and Structural Roles on Triad Patterns in Undirected Networkshttps://www.youtube.com/watch?v=DRc8WO-OBNw
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Session 5
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Graph Clustering with Graph Neural Networkshttps://www.youtube.com/watch?v=-zT0DHB6eW0
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Mining Persistent Activity in Continually Evolving Networkshttps://www.youtube.com/watch?v=0ue2wQSUaDg
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Network Experiment Design for Estimating Direct Treatment Effectshttps://www.youtube.com/watch?v=27I2fCRSWu0
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Robust Unsupervised Mining of Dense Sub-Graphs at Multiple Resolutionshttps://www.youtube.com/watch?v=3J1W5CYy4dw
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Scalable and Consistent Estimation in Continuous-time Networks of Relational Eventshttps://www.youtube.com/watch?v=kaqMGrMWXcI
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Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networkshttps://www.youtube.com/watch?v=tj6H4cvbP5Y
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