node2vec: 推动网络节点特征学习的创新方法

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"node2vec: Scalable Feature Learning for Networks" 在当前的IT领域，网络数据分析和预测任务对于特征工程的需求尤为关键。传统的手动设计特征方法往往无法捕捉到复杂网络中多样化的连接模式。为了突破这一局限，论文《node2vec：网络节点的可扩展特征学习》提出了一个创新算法框架，由斯坦福大学的Aditya Grover和Jure Leskovec共同开发。他们意识到，通过自动学习特征本身，可以显著提升预测任务的自动化水平。 node2vec的核心理念是将网络中的节点映射到一个低维度的特征空间，这个过程旨在最大化保留节点的网络邻域结构。这里的“网络邻域”概念被定义为灵活且动态的，它超越了先前基于固定邻接关系的僵化理解。作者设计了一种带偏置的随机游走策略，这种策略能高效地探索节点的各种邻域，从而增强特征学习的表达能力。随机游走策略的关键在于其灵活性，它能够根据节点的属性和网络结构动态调整，使得学习到的特征不仅反映节点自身的特性，还考虑到其与周围节点的复杂相互作用。相比于单一的邻接矩阵，node2vec能够更好地捕捉到网络中隐藏的社区结构、局部相似性和全局依赖性。通过优化一个目标函数，即最大化模拟真实网络中节点邻域的似然性，node2vec能够在大规模网络上进行有效的特征学习，而不会因为网络规模的增加而导致计算成本急剧上升。这使得它成为处理大型网络数据时的理想工具，尤其是在推荐系统、社交网络分析、社区检测以及节点分类等任务中。 node2vec算法的贡献在于提供了一种高效且通用的方法，用于学习网络节点的特征表示，极大地提升了对网络数据的预测和分析能力，尤其是在处理复杂网络结构多样性时。它的出现不仅革新了网络特征工程的方式，也为未来的研究者和工程师提供了新的思考角度和实践手段。"

node2vec: Scalable Feature Learning for Networks

Aditya Grover

Stanford University

adityag@cs.stanford.edu

Jure Leskovec

Stanford University

jure@cs.stanford.edu

ABSTRACT

Prediction tasks over nodes and edges in networks require careful

effort in engineering features used by learning algorithms. Recent

research in the broader ﬁeld of representation learning has led to

signiﬁcant progress in automating prediction by learning the fea-

tures themselves. However, present feature learning approaches

are not expressive enough to capture the diversity of connectivity

patterns observed in networks.

Here we propose node2vec, an algorithmic framework for learn-

ing continuous feature representations for nodes in networks. In

node2vec, we learn a mapping of nodes to a low-dimensional space

of features that maximizes the likelihood of preserving network

neighborhoods of nodes. We deﬁne a ﬂexible notion of a node’s

network neighborhood and design a biased random walk procedure,

which efﬁciently explores diverse neighborhoods. Our algorithm

generalizes prior work which is based on rigid notions of network

neighborhoods, and we argue that the added ﬂexibility in exploring

neighborhoods is the key to learning richer representations.

We demonstrate the efﬁcacy of node2vec over existing state-of-

the-art techniques on multi-label classiﬁcation and link prediction

in several real-world networks from diverse domains. Taken to-

gether, our work represents a new way for efﬁciently learning state-

of-the-art task-independent representations in complex networks.

Categories and Subject Descriptors: H.2.8 [Database Manage-

ment]: Database applications—Data mining; I.2.6 [Artiﬁcial In-

telligence]: Learning

General Terms: Algorithms; Experimentation.

Keywords: Information networks, Feature learning, Node embed-

dings, Graph representations.

1. INTRODUCTION

Many important tasks in network analysis involve predictions

over nodes and edges. In a typical node classiﬁcation task, we

are interested in predicting the most probable labels of nodes in

a network [33]. For example, in a social network, we might be

interested in predicting interests of users, or in a protein-protein in-

teraction network we might be interested in predicting functional

labels of proteins [25, 37]. Similarly, in link prediction, we wish to

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DOI: http://dx.doi.org/10.1145/2939672.2939754

predict whether a pair of nodes in a network should have an edge

connecting them [18]. Link prediction is useful in a wide variety

of domains; for instance, in genomics, it helps us discover novel

interactions between genes, and in social networks, it can identify

real-world friends [2, 34].

Any supervised machine learning algorithm requires a set of in-

formative, discriminating, and independent features. In prediction

problems on networks this means that one has to construct a feature

vector representation for the nodes and edges. A typical solution in-

volves hand-engineering domain-speciﬁc features based on expert

knowledge. Even if one discounts the tedious effort required for

feature engineering, such features are usually designed for speciﬁc

tasks and do not generalize across different prediction tasks.

An alternative approach is to learn feature representations by

solving an optimization problem [4]. The challenge in feature learn-

ing is deﬁning an objective function, which involves a trade-off

in balancing computational efﬁciency and predictive accuracy. On

one side of the spectrum, one could directly aim to ﬁnd a feature

representation that optimizes performance of a downstream predic-

tion task. While this supervised procedure results in good accu-

racy, it comes at the cost of high training time complexity due to a

blowup in the number of parameters that need to be estimated. At

the other extreme, the objective function can be deﬁned to be inde-

pendent of the downstream prediction task and the representations

can be learned in a purely unsupervised way. This makes the op-

timization computationally efﬁcient and with a carefully designed

objective, it results in task-independent features that closely match

task-speciﬁc approaches in predictive accuracy [21, 23].

However, current techniques fail to satisfactorily deﬁne and opti-

mize a reasonable objective required for scalable unsupervised fea-

ture learning in networks. Classic approaches based on linear and

non-linear dimensionality reduction techniques such as Principal

Component Analysis, Multi-Dimensional Scaling and their exten-

sions [3, 27, 30, 35] optimize an objective that transforms a repre-

sentative data matrix of the network such that it maximizes the vari-

ance of the data representation. Consequently, these approaches in-

variably involve eigendecomposition of the appropriate data matrix

which is expensive for large real-world networks. Moreover, the

resulting latent representations give poor performance on various

prediction tasks over networks.

Alternatively, we can design an objective that seeks to preserve

local neighborhoods of nodes. The objective can be efﬁciently op-

timized using stochastic gradient descent (SGD) akin to backpro-

pogation on just single hidden-layer feedforward neural networks.

Recent attempts in this direction [24, 28] propose efﬁcient algo-

rithms but rely on a rigid notion of a network neighborhood, which

results in these approaches being largely insensitive to connectiv-

ity patterns unique to networks. Speciﬁcally, nodes in networks

arXiv:1607.00653v1 [cs.SI] 3 Jul 2016

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