Senzhang Wang

Influence Maximization Across Partially Aligned Heterogenous Social Networks

The influence maximization problem aims at finding a subset of seed users who can maximize the spread of influence in online social networks (OSNs). Existing works mostly focus on one single homogenous network. However, in the real world, OSNs (1) are usually heterogeneous, via which users can influence each others in multiple channels; and (2) share common users, via whom information could propagate across networks.

Event detection and popularity prediction in microblogging

Abstract As one of the most influential social media platforms, microblogging is becoming increasingly popular in the last decades. Each day a large amount of events appear and spread in microblogging. The spreading of events and corresponding comments on them can greatly influence the public opinion. It is practical important to discover new emerging events in microblogging and predict their future popularity. Traditional event detection and information diffusion models cannot effectively handle our studied problem, because most existing methods focus only on event detection but ignore to predict their future trend. In this paper, we propose a new approach to detect burst novel events and predict their future popularity simultaneously. Specifically, we first detect events from online microblogging stream by utilizing multiple types of information, i.e., term frequency, and user׳s social relation. Meanwhile, the popularity of detected event is predicted through a proposed diffusion model which takes both the content and user information of the event into account. Extensive evaluations on two real-world datasets demonstrate the effectiveness of our approach on both event detection and their popularity prediction.

Citywide traffic congestion estimation with social media

Conventional traffic congestion estimation approaches require the deployment of traffic sensors or large-scale probe vehicles. The high cost of deploying and maintaining these equipments largely limits their spatial-temporal coverage. This paper proposes an alternative solution with lower cost and wider spatial coverage by exploring traffic related information from Twitter. By regarding each Twitter user as a traffic monitoring sensor, various real-time traffic information can be collected freely from each corner of the city. However, there are two major challenges for this problem. Firstly, the congestion related information extracted directly from real-time tweets are very sparse due both to the low resolution of geographic location mentioned in the tweets and the inherent sparsity nature of Twitter data. Secondly, the traffic event information coming from Twitter can be multi-typed including congestion, accident, road construction, etc. It is non-trivial to model the potential impacts of diverse traffic events on traffic congestion. We propose to enrich the sparse real-time tweets from two directions: 1) mining the spatial and temporal correlations of the road segments in congestion from historical data, and 2) applying auxiliary information including social events and road features for help. We finally propose a coupled matrix and tensor factorization model to effectively integrate rich information for Citywide Traffic Congestion Eestimation (CTCE). Extensive evaluations on Twitter data and 500 million public passenger buses GPS data on nearly 700 mile roads of Chicago demonstrate the efficiency and effectiveness of the proposed approach.

Improving the Robustness of Complex Networks with Preserving Community Structure

Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are ‘robust yet fragile’, which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks.

Future Influence Ranking of Scientific Literature

Researchers or students entering a emerging research area are particularly interested in what newly published papers will be most cited and which young researchers will become influential in the future, so that they can catch the most recent advances and find valuable research directions. However, predicting the future importance of scientific articles and authors is challenging due to the dynamic nature of literature networks and evolving research topics. Different from most previous studies aiming to rank the current importance of literatures and authors, we focus on \emph{ranking the future popularity of new publications and young researchers} by proposing a unified ranking model to combine various available information. Specifically, we first propose to extract two kinds of text features, words and words co-occurrence to characterize innovative papers and authors. Then, instead of using static and un-weighted graphs, we construct time-aware weighted graphs to distinguish the various importance of links established at different time. Finally, by leveraging both the constructed text features and graphs, we propose a mutual reinforcement ranking framework called \emph{MRFRank} to rank the future importance of papers and authors simultaneously. Experimental results on the ArnetMiner dataset show that the proposed approach significantly outperforms the baselines on the metric \emph{recommendation intensity}.

PNA: Partial Network Alignment with Generic Stable Matching

To enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. The shared users between different networks are called anchor users, while the remaining unshared users are named as non-anchor users. Connections between accounts of anchor users in different networks are defined as anchor links and networks partially aligned by anchor links can be represented as partially aligned networks. In this paper, we want to predict anchor links between partially aligned social networks, which is formally defined as the partial network alignment problem. The partial network alignment problem is very difficult to solve because of the following two challenges: (1) the lack of general features for anchor links, and (2) the “one - to - one≤” (one to at most one) constraint on anchor links. To address these two challenges, a new method PNA (Partial Network Aligner) is proposed in this paper. PNA (1) extracts various adjacency scores among users across networks based on a set of internetwork anchor meta paths, and (2) utilizes the generic stable matching to identify the non-anchor users to prune the redundant anchor links attached to them. Extensive experiments conducted on two real-world partially aligned social networks demonstrate that PNA can solve the partial network alignment problem very well and outperform all the other comparison methods with significant advantages.

PPNE: Property Preserving Network Embedding

Network embedding aims at learning a distributed representation vector for each node in a network, which has been increasingly recognized as an important task in the network analysis area. Most existing embedding methods focus on encoding the topology information into the representation vectors. In reality, nodes in the network may contain rich properties, which could potentially contribute to learn better representations. In this paper, we study the novel problem of property preserving network embedding and propose a general model PPNE to effectively incorporate the rich types of node properties. We formulate the learning process of representation vectors as a joint optimization problem, where the topology-derived and property-derived objective functions are optimized jointly with shared parameters. By solving this joint optimization problem with an efficient stochastic gradient descent algorithm, we can obtain representation vectors incorporating both network topology and node property information. We extensively evaluate our framework through two data mining tasks on five datasets. Experimental results show the superior performance of PPNE.

Semi-Supervised Network Embedding

Network embedding aims to learn a distributed representation vector for each node in a network, which is fundamental to support many data mining and machine learning tasks such as node classification, link prediction, and social recommendation. Current popular network embedding methods normally first transform the network into a set of node sequences, and then input them into an unsupervised feature learning model to generate a distributed representation vector for each node as the output. The first limitation of existing methods is that the node orders in node sequences are ignored. As a result some topological structure information encoded in the node orders cannot be effectively captured by such order-insensitive embedding methods. Second, given a particular machine learning task, some annotation data can be available. Existing network embedding methods are unsupervised and are not effective to incorporate the annotation data to learn better representation vectors. In this paper, we propose an order sensitive semi-supervised framework for network embedding. Specifically, we first propose an novel order sensitive network embedding method: StructuredNE to integrate node order information into the embedding process in an unsupervised manner. Then based on the annotation data, we further propose an semi-supervised framework SemNE to modify the representation vectors learned by StructuredNE to make them better fit the annotation data. We thoroughly evaluate our framework through three data mining tasks (multi-label classification, network reconstruction and link prediction) on three datasets. Experimental results show the effectiveness of the proposed framework.

Exploiting social circle broadness for influential spreaders identification in social networks

Influential spreaders identification in social networks contributes to optimize the use of available resources and ensure the more efficient spread of information. In contrast to common belief that highly connected or core located users are most crucial spreaders, this paper shows that both user’s local and global structural properties matter in information diffusion. We propose a new metric, social circle broadness, to measure a user’s information spreading influence by qualitatively combining the two above properties. Firstly, a definition of social circle diversity is introduced to measure the dispersion extent of a user’s friends distribution in the network. Based on it, a method to calculate each user’s local social circle broadness is presented. Preliminary experiments on a coauthor dataset demonstrate the effectiveness of social circle broadness in information diffusion. Furthermore, a social circle weighted PageRank (SCWPR) algorithm is proposed to iteratively rank each user’s global social circle broadness. We conduct extensive comparison experiments against six state-of-the-art baseline methods on four real social network datasets. The results show that SCWPR outperforms all of them for influential spreaders identification in information propagation.

Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data

Estimating urban traffic conditions of an arterial network with GPS probe data is a practically important while substantially challenging problem, and has attracted increasing research interests recently. Although GPS probe data is becoming a ubiquitous data source for various traffic related applications currently, they are usually insufficient for fully estimating traffic conditions of a large arterial network due to the low sampling frequency. To explore other data sources for more effectively computing urban traffic conditions, we propose to collect various traffic events such as traffic accident and jam from social media as complementary information. In addition, to further explore other factors that might affect traffic conditions, we also extract rich auxiliary information including social events, road features, Point of Interest (POI), and weather. With the enriched traffic data and auxiliary information collected from different sources, we first study the traffic co-congestion pattern mining problem with the aim of discovering which road segments geographically close to each other are likely to co-occur traffic congestion. A search tree based approach is proposed to efficiently discover the co-congestion patterns. These patterns are then used to help estimate traffic congestions and detect anomalies in a transportation network. To fuse the multisourced data, we finally propose a coupled matrix and tensor factorization model named TCE_R to more accurately complete the sparse traffic congestion matrix by collaboratively factorizing it with other matrices and tensors formed by other data. We evaluate the proposed model on the arterial network of downtown Chicago with 1,257 road segments whose total length is nearly 700 miles. The results demonstrate the superior performance of TCE_R by comprehensive comparison with existing approaches.