A new graph-based method for pairwise global network alignment

Online social networks can often be represented as heterogeneous information networks containing abundant information about: who, where, when and what. Nowadays, people are usually involved in multiple social networks simultaneously. The multiple accounts of the same user in different networks are mostly isolated from each other without any connection between them. Discovering the correspondence of these accounts across multiple social networks is a crucial prerequisite for many interesting inter-network applications, such as link recommendation and community analysis using information from multiple networks. In this paper, we study the problem of anchor link prediction across multiple heterogeneous social networks, i.e., discovering the correspondence among different accounts of the same user. Unlike most prior work on link prediction and network alignment, we assume that the anchor links are one-to-one relationships (i.e., no two edges share a common endpoint) between the accounts in two social networks, and a small number of anchor links are known beforehand. We propose to extract heterogeneous features from multiple heterogeneous networks for anchor link prediction, including user's social, spatial, temporal and text information. Then we formulate the inference problem for anchor links as a stable matching problem between the two sets of user accounts in two different networks. An effective solution, Mna (Multi-Network Anchoring), is derived to infer anchor links w.r.t. the one-to-one constraint. Extensive experiments on two real-world heterogeneous social networks show that our Mna model consistently outperform other commonly-used baselines on anchor link prediction.

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“…Network alignment problem has also been studied by many works in recent years, which has many applications bioinformatics [11,19]. Bayati et.…”

Section: Related Workmentioning

confidence: 99%

Inferring anchor links across multiple heterogeneous social networks

Kong

Zhang

2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

308

256

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“…This corresponds to a QAP instance with a symmetric and sparse weight matrix. We improve upon an existing Lagrangian relaxation approach presented in previous work [7] to obtain strong upper and lower bounds for the problem. We exploit the closeness to QAP and generalize a dual descent method for updating the Lagrangian multipliers to the generalized problem.…”

Section: Contributionmentioning

confidence: 99%

“…The IsoRank algorithm by Singh et al [6] formulates the global alignment problem as an eigenvalue problem, which preferentially matches nodes with similar neighborhood. Klau [7] presented Natalie, the predecessor of Natalie 2.0, which is described in detail in this paper. The methods are based on an integer linear programming approach solved by Lagrangian relaxation.…”

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confidence: 99%

Natalie 2.0: Sparse Global Network Alignment as a Special Case of Quadratic Assignment

2015

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Data on molecular interactions is increasing at a tremendous pace, while the development of solid methods for analyzing this network data is still lagging behind. This holds in particular for the field of comparative network analysis, where one wants to identify commonalities between biological networks. Since biological functionality primarily operates at the network level, there is a clear need for topology-aware comparison methods. We present a method for global network alignment that is fast and robust and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account. We exploit that network alignment is a special case of the well-studied quadratic assignment problem (QAP). We focus on sparse network alignment, where each node can be mapped only to a typically small subset of nodes in the other network. This corresponds to a QAP instance with a symmetric and sparse weight matrix. We obtain strong upper and lower bounds for the problem by improving a Lagrangian relaxation approach and introduce the open source software tool Natalie 2.0, a publicly Algorithms 2015, 8 1036 available implementation of our method. In an extensive computational study on protein interaction networks for six different species, we find that our new method outperforms alternative established and recent state-of-the-art methods.

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“…For instance Zaslavskiy et al (2009) redefined the problem of global alignment as a standard graph matching problem and investigated methods using ideas and approaches from state-of-the-art graph matching techniques. (Klau, 2009) formalized global network alignment as an integer linear programming problem, where a near-optimal solution with a quality guarantee is found by solving a Lagrangian relaxation of the original optimization formulation. Recently, Chindelevitch et al (2010) proposed a method where the global alignment is encoded as bipartite matching and applied a very efficient local optimization heuristic used for the well-known Travelling Salesman Problem.…”

Section: Global Pairwise Network Alignmentmentioning

confidence: 99%