2-task Classification

The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. Source: https://arxiv.org/pdf/1706.02216.pdf Image Source: https://minimaxir.com/2016/05/reddit-graph/

Reddit-5K is a relational dataset extracted from Reddit.

Reddit12k contains 11929 graphs each corresponding to an online discussion thread where nodes represent users, and an edge represents the fact that one of the two users responded to the comment of the other user. There is 1 of 11 graph labels associated with each of these 11929 discussion graphs, representing the category of the community. Source: Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity

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TeleSim is a network-aware hardware-in-the-loop dataset designed to evaluate the performance of telerobotic systems under varying network conditions. It includes 300 fine-manipulation trials using a 6-DoF robotic arm and simulated networks in OMNeT++. Each trial captures: - Network configuration (bandwidth, latency, jitter, packet loss) - Measured network metrics (throughput, delay, jitter, real packet loss) - Video quality (PSNR, SSIM) - Task performance (completion time, success rate) Motivation: Existing teleoperation datasets often ignore the impact of real-world network degradation. TeleSim fills this gap by systematically evaluating how adverse conditions affect visual perception and task execution. Use cases: - Benchmarking teleoperation performance - Designing adaptive control algorithms - Evaluating network resilience of robotic systems