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Stanford图机器学习公开课CS224W(四)笔记

Lecture 4 -Community Steructure in Networks

Community Steructure in Networks 一图胜过万言,上节课研究Structural roles,这节课研究Communities。 一. Granovetter 定理 这个定理其实就是他定义的结论。 Granovetter对于一条边分别在社会和结构上之间的功能做了分析: 首先从结构特征上来看: 在结构上扮演“朋友”的边通常是强社交的;跨不同网络部分的长范...

Stanford图机器学习公开课CS224W(三)笔记

Lecture 3 -Motifs and Structural Roles in Networks

Motifs and Structural Roles in Networks 一. Subgraphs, Motifs 1.1 Network Motifs 这里将Network Motifs 定义为:网络连接中重复且重要的模式。这些属性可以帮助我们理解这个网络如何工作和预测网络的功能。 1.导出子图: 导出子图G’,V’∈V,但对于V’中任一顶点,只要在原图G中有对应边,那么就要出...

Stanford图机器学习公开课CS224W(二)笔记

Lecture 2 -Properties of Networks & Random Graph Models

Properties of Networks & Random Graph Models 1. 网络的属性:如何来度量一个网络 For 无向图,对于有向图很容易泛化 1.1 度分布(Degree Distribution) Degree distribution $P(k)$: 一个随机选取的节点,其度为k的概率。 $N_k$为度为k的节点个数,$P(k) = N_...

Stanford图机器学习公开课CS224W(一)笔记

Lecture 1 -Machine Learning with Graphs.

Lecture 1 -Machine Learning with Graphs. 一.图基础知识 1.节点的度(degree) 设节点的度用$k_i$表示。 无向图 :一条边贡献2个度,故平均度为$ \bar{k}=\langle k\rangle=\frac{1}{N} \sum_{i=1}^{N} k_{i}=\frac{2 E}{N}$ 有向图: 度分为入度和出度,因此$\bar{...

PDiff:Semantic-based Patch Presence Testing for Downstream Kernels

二进制代码相似性检测

PDiff: Semantic-based Patch Presence Testing for Downstream Kernels 作者:Zheyue Jiang,Yuan Zhang,Jun Xu,Qi Wen,Zhenghe Wang,Xiaohan Zhang,Xinyu Xing,Min Yang,Zhemin Yang 单位:复旦大学 出处:CCS 2020 论文:pap...

RTFM! Automatic Assumption Discovery and Verification Derivation from Library Document for API Misuse Detection

自动化程序分析

RTFM! Automatic Assumption Discovery and Verification Derivation from Library Document for API Misuse Detection 作者:Tao Lv, Ruishi Li, Yi Yang, Kai Chen, Xiaojing Liao, XiaoFeng Wang, Peiwei Hu...

Similarity Metric Method for Binary Basic Blocks of Cross-Instruction Set Architecture

二进制代码相似性检测

Similarity Metric Method for Binary Basic Blocks of Cross-Instruction Set Architecture https://github.com/zhangxiaochuan/MIRROR 期刊/会议: NDSS20 BAR workshop ...

VulSeeker-Pro:Enhanced Semantic Learning Based Binary Vulnerability Seeker with Emulation

二进制代码相似性检测

VulSeeker-Pro:Enhanced Semantic Learning Based Binary Vulnerability Seeker with Emulation 期刊/会议: ESEC/FSE ’18 发表时间: 2018年11月4 发表机构:...

Binary Similarity Detection Using Machine Learning

二进制代码相似性检测

Binary Similarity Detection Using Machine Learning 期刊/会议: CCS PLAS’18 session 发表时间: 2018年12月19 发表机构: Cornell Tech & Tel-A...

DEEPBINDIFF:Learning Program-Wide Code Representations for Binary Diffing

二进制代码相似性检测

DEEPBINDIFF: Learning Program-Wide Code Representations for Binary Diffing https://github.com/deepbindiff/DeepBinDiff 期刊/会议: NDSS2020 发表时间: 202...