Explainable, Stable, and Scalable Network Embedding Algorithms for Unsupervised Learning of Graph Representations

Lu, Ping-En; Yeh, Chih‐Kuang; Chang, Cheng-Shang

doi:10.1109/tcss.2022.3181739

Cited by 1 publication

(1 citation statement)

References 52 publications

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“…For the political blogs, there are 1224 nodes and 16 715 edges. For the ego-Facebook dataset, we remove multiple edges, self-loops, and nodes that are not in the largest component of the network as in [30] . By doing so, there are 2851 nodes and 62 318 edges left in the network.…”

Section: Numerical Resultsmentioning

confidence: 99%

Positively Correlated Samples Save Pooled Testing Costs

Lin

Liu

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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The group testing approach, which achieves significant cost reduction over the individual testing approach, has received a lot of interest lately for massive testing of COVID-19. Many studies simply assume samples mixed in a group are independent. However, this assumption may not be reasonable for a contagious disease like COVID-19. Specifically, people within a family tend to infect each other and thus are likely to be positively correlated. By exploiting positive correlation, we make the following two main contributions. One is to provide a rigorous proof that further cost reduction can be achieved by using the Dorfman two-stage method when samples within a group are positively correlated. The other is to propose a hierarchical agglomerative algorithm for pooled testing with a social graph, where an edge in the social graph connects frequent social contacts between two persons. Such an algorithm leads to notable cost reduction (roughly 20-35%) compared to random pooling when the Dorfman two-stage algorithm is applied.

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Section: Numerical Resultsmentioning

confidence: 99%