大数据时代下的节点耦合聚类链路预测策略
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节点耦合聚类的链路预测方法是一篇研究论文，关注于在大数据时代背景下网络中的链接预测问题。随着现实世界网络中潜在信息的重要性日益凸显，链接预测已经成为多个学科领域的研究热点。在大数据的浪潮下，链接预测面临着诸多挑战，包括如何高效地处理大规模数据、如何准确地识别出隐藏的连接模式以及如何应对数据稀疏性等问题。
本文的主要贡献在于提出了一种名为"Node-coupling clustering"的方法，这种方法旨在通过节点之间的耦合关系来改进链接预测的精度。传统的链接预测往往依赖于单一的节点属性或者局部结构，而节点耦合聚类则更加强调跨节点间的协同作用，通过挖掘节点之间的共同特征和相互影响，来预测潜在的连接可能性。该方法可能涉及到数据挖掘技术，如协同过滤、社区发现、网络嵌入等，以捕捉网络中的复杂关系。
文章流程可能包括以下几个步骤：
1. **背景介绍**：首先概述链接预测的基本概念，以及在大数据环境下其重要性和应用领域，比如社交网络分析、推荐系统和生物网络预测。
2. **方法论**：详细介绍节点耦合聚类算法的原理，可能包括节点特征的选择、聚类过程（如层次聚类、谱聚类或基于图的聚类）、耦合度量的定义以及如何将聚类结果与链接预测相结合。
3. **实验设计**：展示如何在大规模真实网络数据集上实施该方法，并可能对比其他常用的链接预测算法，以评估其性能。
4. **结果与分析**：报告预测结果，分析模型的准确性和鲁棒性，以及对不同参数设置的敏感性分析。
5. **讨论与局限性**：探讨方法的优点和不足，可能涉及可扩展性、计算效率和对噪声数据的处理。
6. **结论与未来工作**：总结研究成果，提出进一步改进或拓展该方法的方向，例如集成更多元的数据源或结合深度学习技术。
这篇论文对于理解如何在大数据环境中利用节点耦合聚类进行有效的链路预测具有重要的理论价值和实践意义，为解决实际网络分析中的挑战提供了新的思路和工具。
AUC ¼
H þ 0:5 E
N
ð2Þ
3.2.2. Precision
This metric considers N links with the highest S
xy
values in all
unknown links. If there are T existing yet unknown links in the
top N unknown links [5,8], the Precision is defined as:
Precision ¼
T
N
ð3Þ
4. Node-coupling clustering approaches
In this section, we present our approaches for link prediction.
Firstly, we present a new node-coupling degree metric – node-
coupling clustering coefficient. Then, we present the process of
our approaches. Finally, we give the complexity analysis of our
approaches.
4.1. Node-coupling clustering coefficient
Many similarity-based methods only consider the number or
degrees of common neighbor nodes of a predicted node-pair in link
prediction, and few exploit further the coupling degrees among the
common neighbor nodes and the clustering information to
improve the prediction accuracy. Based on the above reason, we
propose a new node-coupling degree metric – node-coupling clus-
tering coefficient (NCCC), which can capture the clustering infor-
mation of a network and evaluate the coupling degrees between
the common neighbor nodes of a predicted node-pair. It also con-
siders different roles of the common neighbor nodes of a predicted
node-pair in a network. Now, we introduce this metric through a
simple example.
Fig. 1 shows an example for predicting the link between nodes
M and N in two networks. Two original networks are described in
Fig. 1(a) and (b). Fig. 1(c) and (d) are two subgraphs that consist of
nodes M; N and their common neighbors in Fig. 1(a) and (b),
respectively. We aim to predict which link between nodes M and
N is more likely to exist in Fig. 1(a) and (b). In general, we find that
the coupling degrees of nodes M; N and their common neighbors
are higher in Fig. 1(c) than (d). Thus, we believe the link of nodes
M; N in Fig. 1(a) is more likely to exist than in Fig. 1(b). If we apply
CN; AA; RA; PA to predict the link of nodes M; N in these two orig-
inal networks, we can gain the same prediction result for each
method. The reasons are as follows: from Fig. 1(a) and (b), we
can see that the common neighbor set fbdf g of nodes M; N are
the same, and every corresponding common neighbor node has
the same degree value in these two original networks. The similar-
ity metric is the number of the common neighbor nodes of a pre-
dicted node-pair in CN. CN has the same prediction results
because of the same common neighbor node set fbdf g of nodes
M; N in these two original networks.
RA; AA are based on the
degree values of the common neighbor nodes. RA has the same
prediction probability as AA because of the same degree value of
every corresponding common neighbor node in fbdf g in these
two original networks. For the same reason, PA provides the same
prediction result because that there are the corresponding same
degree values for nodes M and N in these two original networks.
However, the link probabilities between node M and node N in
Fig. 1(a) and (b) are not likely to be the same.
In the above case, inspired by [10,19], we propose a new node-
coupling degree metric based on the clustering information and
node degree – node-coupling clustering coefficient. This metric
cannot only resolve the above prediction problem in Fig. 1, but also
capture the clustering information of a network. If node n is a com-
mon neighbor node of the predicted node-pair ðM; NÞ, the node-
coupling clustering coefficient of node n; NCCCðnÞ, can be defined
as follows:
NCCCðnÞ¼
P
i2C
n
1
d
i
þ CðiÞ
P
j2
C
ðnÞ
1
d
j
þ CðjÞ
ð4Þ
where CðnÞ is the neighbor node set of node n. ðM; NÞ denotes a pre-
dicted node-pair. n 2
CðMÞ\CðNÞ. C
n
denotes the common neigh-
bor node set of the node-pair ðM; NÞ in
CðnÞ, which includes
nodes M; N. Namely C
n
¼ CðMÞ\CðNÞ\CðnÞ[fM; Ng. d
i
denotes
the degree value of node i. CðiÞ denotes the clustering coefficient
of node i. In this metric,
1
d
i
þ CðiÞ is considered as the contribution
of node i to the coupling degree of the common neighbor nodes of
the predicted node-pair ðM; NÞ. The node-coupling clustering coeffi-
cient of node n is the ratio of the contribution sum of all nodes in C
n
to that in CðnÞ. In this way, our approaches can apply this metric
that incorporates the clustering information and different roles of
each related node to improve the prediction accuracy for link
prediction.
In Eq. (4), since C
n
# CðnÞ,
P
i2C
n
1
d
i
þ CðiÞ
6
P
j2
C
ðnÞ
1
d
j
þ Cð jÞ
.
As a result, NCCCðnÞ2ð0; 1. Specially, NCCCðnÞ¼1 when
C
n
¼ CðnÞ.
4.2. Node-coupling clustering approach based on probability theory
(NCCPT)
From probability theory, we propose a new link prediction
approach based on the node-coupling clustering coefficient (NCCC)
in Section 4.1. Given a pair of predicted nodes ðx; yÞ, node n is a com-
mon neighbor node of the node-pair ðx; yÞ. NCCCðnÞ can be consid-
ered as the contribution of node n to the connecting probability for
the predicted node-pair ðx; yÞ. PðnÞ denotes the link existence
probability that node x and node y connect because of node n.
PðnÞ
denotes the link non-existence probability that node n
connects node x to node y. Therefore, PðnÞ¼NCCCðnÞ and
PðnÞ¼1 NCCCðnÞ. fA
1
; A
2
; ...; A
i
; ...; A
m
g is the common neighbor
set of the predicted node-pair ðx; yÞ, namely
CðxÞ\CðyÞ¼
fA
1
; A
2
; ...; A
i
; ...; A
m
g. We assume that these common neighbor
nodes of the node-pair ðx; yÞ are independent to each other. If there
Fig. 1. An example for predicting the link between nodes M and N in two original networks.
F. Li et al. / Knowledge-Based Systems 89 (2015) 669–680
671
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