SlowGuess/NeurIPS_2023_OCR huggingface dataset:
The time consumption of CPL on node classification and link prediction are shown in Table 6 and Table 7 respectively.
E Case study of CPL on link prediction
Error bound: In the case study, the recorded confidence threshold is  $1 - q = 0.98$  for WikiCS. We adopt 5 views of dropout with the augmentation drop rate 0.05. And according to the error bound given by Theorem 2.3, given the confidence threshold, Eq.2 suggests that the higher prediction consistency should lead to a smaller error bound. The final prediction consistency is  $\mathcal{A}(g) = 0.0358$ , thus, we can calculate error bound  $Err(g) = 0.1116$ . The AUC and AP are  $95.56 \pm 0.24\%$ ,  $95.58 \pm 0.29\%$  which are bounded within  $Err(g)$ .
Knowledge discovery: In the 5 random experiments, we add 500 pseudo links in each iteration. Here we focus on the common PL links in the first iteration, which are considered the most confident samples. We look for the metadata of WikiCS whose node, feature, link and node label represent paper, token, reference relation and topic of the paper respectively. There are These 7 most confident links categorized into 2 groups. We take 3 out of 5 nodes in group1 and the 2 nodes in group2 for analysis, whose detailed information of these nodes is shown in AppendixE.
Table 8: Details of Node Information in the Case Study.
Node	Group 1	Group 2
Node 2702	Node 5688	Node 8906	Node 3489	Node 7680
ID	17505908	11353631	30138652	23221074	12265137
Outlinks	[6097297]	[6097297]	[244374, 6097297]	[20901]	[]
Title	Ubuntu Hacks	Pungi (software)	LinuxPAE64	Malware Bell	Norton Confidential
Label	Operating systems "ubuntu", "hacks", "ubuntu", "hacks", "tips", "tools", "exploring", "using", "tuning", "linux", "book", "tips", "ubuntu", "popular", "linux", "distribution", "release", "7", "upwards"	Operating systems "pungi", "software", "pungi", "program", "making", "spins", "fedora", "linux","distribution", "release", "7", "upwards"	Operating systems "linuxpae64", "lin-uxpae64", "port", "linux", "kernel", "running", "com-patibility", "mode", "x86-64", "pro-cessor", "kernel", "capable", "loading", "i386", "modules", "device", "drivers", "supports", "64-bit", "linux", "appli-cations", "user", "mode"	malware security "malware", "bell", "malware", "bell", "malware", "program", "made", "tai-wan", "somewhere", "2006", "2007", "malware", "bell", "tries", "install", "automatically", "upon", "visiting", "website", "promoting", "con-taining", "malware"	"confidential", "norton", "confidential", "norton", "confidential", "pro-gram", "designed", "encrypt", "pass-words", "online", "detect", "phishing", "sites"
For group 1, 3 nodes are connected by the pseudo links, and they are all linked to a central node whose degree is 321. The metadata information of the nodes are all strongly relevant to "Linux" in the "operating systems" topic. Thus, the PL linked nodes are likely to have common neighbors discovered triangle relationship. In group2, node 3489 has no in/out degree and is pseudo linked to node 7680. Both papers focus on the "malware"/"phishing" under the topic "Computer security". Although they only have one common token, the CPL strategy successfully discovers the correlation and consistently add it to the training set. The detailed result of the case study is shown in Table 8.