Chun Ta Lu

Inferring the impacts of social media on crowdfunding

Crowdfunding -- in which people can raise funds through collaborative contributions of general public (i.e., crowd) -- has emerged as a billion dollars business for supporting more than one million ventures. However, very few research works have examined the process of crowdfunding. In particular, none has studied how social networks help crowdfunding projects to succeed. To gain insights into the effects of social networks in crowdfunding, we analyze the hidden connections between the fundraising results of projects on crowdfunding websites and the corresponding promotion campaigns in social media. Our analysis considers the dynamics of crowdfunding from two aspects: how fundraising activities and promotional activities on social media simultaneously evolve over time, and how the promotion campaigns influence the final outcomes. From our investigation, we identify a number of important principles that provide a useful guide for devising effective campaigns. For example, we observe temporal distribution of customer interest, strong correlations between a crowdfunding projects early promotional activities and the final outcomes, and the importance of concurrent promotion from multiple sources. We then show that these discoveries can help predict several important quantities, including overall popularity and the success rate of the project. Finally, we show how to use these discoveries to help design crowdfunding sites.

Identifying Your Customers in Social Networks

Personal social networks are considered as one of the most influential sources in shaping a customers attitudes and behaviors. However, the interactions with friends or colleagues in social networks of individual customers are barely observable in most e-commerce companies. In this paper, we study the problem of customer identification in social networks, i.e., connecting customer accounts at e-commerce sites to the corresponding user accounts in online social networks such as Twitter. Identifying customers in social networks is a crucial prerequisite for many potential marketing applications. These applications, for example, include personalized product recommendation based on social correlations, discovering community of customers, and maximizing product adoption and profits over social networks. We introduce a methodology CSI (Customer-Social Identification) for identifying customers in online social networks effectively by using the basic information of customers, such as username and purchase history. It consists of two key phases. The first phase constructs the features across networks that can be used to compare the similarity between pairs of accounts across networks with different schema (e.g. an e-commerce company and an online social network). The second phase identifies the top-K maximum similar and stable matched pairs of accounts across partially aligned networks. Extensive experiments on real-world datasets show that our CSI model consistently outperforms other commonly-used baselines on customer identification.

Joint Community and Structural Hole Spanner Detection via Harmonic Modularity

Detecting communities (or modular structures) and structural hole spanners, the nodes bridging different communities in a network, are two essential tasks in the realm of network analytics. Due to the topological nature of communities and structural hole spanners, these two tasks are naturally tangled with each other, while there has been little synergy between them. In this paper, we propose a novel harmonic modularity method to tackle both tasks simultaneously. Specifically, we apply a harmonic function to measure the smoothness of community structure and to obtain the community indicator. We then investigate the sparsity level of the interactions between communities, with particular emphasis on the nodes connecting to multiple communities, to discriminate the indicator of SH spanners and assist the community guidance. Extensive experiments on real-world networks demonstrate that our proposed method outperforms several state-of-the-art methods in the community detection task and also in the SH spanner identification task (even the methods that require the supervised community information). Furthermore, by removing the SH spanners spotted by our method, we show that the quality of other community detection methods can be further improved.

Identifying connectivity patterns for brain diseases via multi-side-view guided deep architectures

There is considerable interest in mining neuroimage data to discover clinically meaningful connectivity patterns to inform an understanding of neurological and neuropsychiatric disorders. Subgraph mining models have been used to discover connected subgraph patterns. However, it is difficult to capture the complicated interplay among patterns. As a result, classification performance based on these results may not be satisfactory. To address this issue, we propose to learn non-linear representations of brain connectivity patterns from deep learning architectures. This is non-trivial, due to the limited subjects and the high costs of acquiring the data. Fortunately, auxiliary information from multiple side views such as clinical, serologic, immunologic, cognitive and other diagnostic testing also characterizes the states of subjects from different perspectives. In this paper, we present a novel Multi-side-View guided AutoEncoder (MVAE) that incorporates multiple side views into the process of deep learning to tackle the bias in the construction of connectivity patterns caused by the scarce clinical data. Extensive experiments show that MVAE not only captures discriminative connectivity patterns for classification, but also discovers meaningful information for clinical interpretation.

Online Unsupervised Multi-view Feature Selection

In this paper, we propose an Online unsupervised Multi-View Feature Selection method, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via nonnegative matrix factorization with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods.

Multilinear Factorization Machines for Multi-Task Multi-View Learning

Many real-world problems, such as web image analysis, document categorization and product recommendation, often exhibit dual-heterogeneity: heterogeneous features obtained in multiple views, and multiple tasks might be related to each other through one or more shared views. To address these Multi-Task Multi-View (MTMV) problems, we propose a tensor-based framework for learning the predictive multilinear structure from the full-order feature interactions within the heterogeneous data. The usage of tensor structure is to strengthen and capture the complex relationships between multiple tasks with multiple views. We further develop efficient multilinear factorization machines (MFMs) that can learn the task-specific feature map and the task-view shared multilinear structures, without physically building the tensor. In the proposed method, a joint factorization is applied to the full-order interactions such that the consensus representation can be learned. In this manner, it can deal with the partially incomplete data without difficulty as the learning procedure does not simply rely on any particular view. Furthermore, the complexity of MFMs is linear in the number of parameters, which makes MFMs suitable to large-scale real-world problems. Extensive experiments on four real-world datasets demonstrate that the proposed method significantly outperforms several state-of-the-art methods in a wide variety of MTMV problems.

Spatio-temporal tensor analysis for whole-brain fMRI classification

Owing to prominence as a research and diagnostic tool in human brain mapping, whole-brain fMRI image analysis has been the focus of intense investigation. Conventionally, input fMRI brain images are converted into vectors or matrices and adapted in kernel based classifiers. fMRI data, however, are inherently coupled with sophisticated spatio-temporal tensor structure (i.e., 3D space × time). Valuable structural information will be lost if the tensors are converted into vectors. Furthermore, time series fMRI data are noisy, involving time shift and low temporal resolution. To address these analytic challenges, more compact and discriminative representations for kernel modeling are needed. In this paper, we propose a novel spatio-temporal tensor kernel (STTK) approach for whole-brain fMRI image analysis. Specifically, we design a volumetric time series extraction approach to model the temporal data, and propose a spatio-temporal tensor based factorization for feature extraction. We further leverage the tensor structure to encode prior knowledge in the kernel. Extensive experiments using real-world datasets demonstrate that our proposed approach effectively boosts the fMRI classification performance in diverse brain disorders (i.e., Alzheimer’s disease, ADHD and HIV).

Community detection with partially observable links and node attributes

Community detection has been an important task for social and information networks. Existing approaches usually assume the completeness of linkage and content information. However, the links and node attributes can usually be partially observable in many real-world networks. For example, users can specify their privacy settings to prevent non-friends from viewing their posts or connections. Such incompleteness poses additional challenges to community detection algorithms. In this paper, we aim to detect communities with partially observable link structure and node attributes. To fuse such incomplete information, we learn link-based and attribute-based representations via kernel alignment and a co-regularization approach is proposed to combine the information from both sources (i.e., links and attributes). The link-based and attribute-based representations can lend strength to each other via the partial consensus learning. We present two instantiations of this framework by enforcing hard and soft consensus constraint respectively. Experimental results on real-world datasets show the superiority of the proposed approaches over the baseline methods and its robustness under different observable levels.

Collective Geographical Embedding for Geolocating Social Network Users

Inferring the physical locations of social network users is one of the core tasks in many online services, such as targeted advertisement, recommending local events, and urban computing. In this paper, we introduce the Collective Geographical Embedding (CGE) algorithm to embed multiple information sources into a low dimensional space, such that the distance in the embedding space reflects the physical distance in the real world. To achieve this, we introduced an embedding method with a location affinity matrix as a constraint for heterogeneous user network. The experiments demonstrate that the proposed algorithm not only outperforms traditional user geolocation prediction algorithms by collectively extracting relations hidden in the heterogeneous user network, but also outperforms state-of-the-art embedding algorithms by appropriately casting geographical information of check-in.

Multi-view Clustering with Graph Embedding for Connectome Analysis

Multi-view clustering has become a widely studied problem in the area of unsupervised learning. It aims to integrate multiple views by taking advantages of the consensus and complimentary information from multiple views. Most of the existing works in multi-view clustering utilize the vector-based representation for features in each view. However, in many real-world applications, instances are represented by graphs, where those vector-based models cannot fully capture the structure of the graphs from each view. To solve this problem, in this paper we propose a Multi-view Clustering framework on graph instances with Graph Embedding (MCGE). Specifically, we model the multi-view graph data as tensors and apply tensor factorization to learn the multi-view graph embeddings, thereby capturing the local structure of graphs. We build an iterative framework by incorporating multi-view graph embedding into the multi-view clustering task on graph instances, jointly performing multi-view clustering and multi-view graph embedding simultaneously. The multi-view clustering results are used for refining the multi-view graph embedding, and the updated multi-view graph embedding results further improve the multi-view clustering. Extensive experiments on two real brain network datasets (i.e., HIV and Bipolar) demonstrate the superior performance of the proposed MCGE approach in multi-view connectome analysis for clinical investigation and application.