Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training graphs and testing graphs, inducing the degeneration of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings. The fundamental reason for such degeneration is that most GNNs are developed based on the I.I.D hypothesis. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation. This learning mechanism inherits from the common characteristics of machine learning approaches. However, such spurious correlations may change in the wild testing environments, leading to the failure of GNNs. Therefore, eliminating the impact of spurious correlations is crucial for stable GNN models. To this end, in this paper, we argue that the spurious correlation exists among subgraph-level units and analyze the degeneration of GNN in causal view. Based on the causal view analysis, we propose a general causal representation framework for stable GNN, called StableGNN. The main idea of this framework is to extract high-level representations from raw graph data first and resort to the distinguishing ability of causal inference to help the model get rid of spurious correlations. Particularly, to extract meaningful high-level representations, we exploit a differentiable graph pooling layer to extract subgraph-based representations by an end-to-end manner. Furthermore, inspired by the confounder balancing techniques from causal inference, based on the learned high-level representations, we propose a causal variable distinguishing regularizer to correct the biased training distribution by learning a set of sample weights. Hence, GNNs would concentrate more on the true connection between discriminative substructures and labels. Extensive experiments are conducted on both synthetic datasets with various distribution shift degrees and eight real-world OOD graph datasets. The results well verify that the proposed model StableGNN not only outperforms the state-of-the-arts but also provides a flexible framework to enhance existing GNNs. In addition, the interpretability experiments validate that StableGNN could leverage causal structures for predictions.

Most existing graph neural networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with the test set. In reality, the test data are not even available during the training process, making selection bias agnostic. Training GNNs with biased selected nodes leads to significant parameter estimation bias and greatly impacts the generalization ability on test nodes. In this article, we first present an experimental investigation, which clearly shows that the selection bias drastically hinders the generalization ability of GNNs, and theoretically proves that the selection bias will cause the biased estimation on GNN parameters. Then to remove the bias in GNN estimation, we propose a novel debiased GNNs (DGNN) with a differentiated decorrelation regularizer. The differentiated decorrelation regularizer estimates a sample weight for each labeled node such that the spurious correlation of learned embeddings could be eliminated. We analyze the regularizer in causal view and it motivates us to differentiate the weights of the variables based on their contribution to the confounding bias. Then, these sample weights are used for reweighting GNNs to eliminate the estimation bias, and thus, help to improve the stability of prediction on unknown test nodes. Comprehensive experiments are conducted on several challenging graph datasets with two kinds of label selection biases. The results well verify that our proposed model outperforms the state-of-the-art methods and DGNN is a flexible framework to enhance existing GNNs.

Graph neural network (GNN), as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. Recently, some works attempt to generalize GNN to heterogeneous graph which contains different types of nodes and links. Heterogeneous graph neural networks (HeteGNNs) usually follow two steps: aggregate neighbors via single meta-path and then aggregate rich semantics via multiple meta-paths. However, we discover an important semantic confusion phenomenon in HeteGNNs, i.e., with the growth of model depth, the learned node embeddings become indistinguishable, leading to the performance degradation of HeteGNNs. We explain semantic confusion by theoretically deriving that HeteGNNs and multiple meta-paths based random walk are essentially equivalent. Following the theoretical analysis, we propose a novel Heterogeneous graph Propagation Network (HPN) to alleviate the semantic confusion. Specically, the semantic propagation mechanism of HPN absorbes nodes local semantic with a proper weight during aggregating process, which makes HPN capture the characteristics of each node and learn distinguishable node embedding with deeper HeteGNN architecture. Then, the semantic fusion mechanism is designed to learn the importance of meta-path and fuse them judiciously. Extensive experimental results on three datasets show the superior performance of the proposed HPN over the state-of-the-arts.

The prosperous development of social e-commerce has spawned diverse recommendation demands, and accompanied a new recommendation paradigm, share recommendation. Significantly different from traditional binary recommendations (e.g., item recommendation and friend recommendation), share recommendation models ternary interactions among 〈 User, Item, Friend 〉 , which aims to recommend a most likely friend to a user who would like to share a specific item, progressively becoming an indispensable service in social e-commerce. Seamlessly integrating the social relations and purchase behaviours, share recommendation improves user stickiness and monetizes the user influence, meanwhile encountering three unique challenges: rich heterogeneous information, complex ternary interaction, and asymmetric share action. In this paper, we first study the share recommendation problem and propose a heterogeneous graph neural network based share recommendation model, called HGSRec. Specifically, HGSRec delicately designs a tripartite heterogeneous GNNs to describe the multifold characteristics of users and items, and then dynamically fuses them via capturing potential ternary dependency with a dual co-attention mechanism, followed by a transitive triplet representation to depict the asymmetry of share action and predict whether share action happens. Offline experiments demonstrate the superiority of the proposed HGSRec with significant improvements (11.7%-14.5%) over the state-of-the-arts, and online A/B testing on Taobao platform further demonstrates the high industrial practicability and stability of HGSRec.

Graphs are ubiquitous and play an essential role in the real world. A vital prerequisite for analyzing graphs is to learn their effective representations. Most existing graph representation learning models are hand-crafted, which lack the scalability to different kinds of graphs. In this paper, we present AutoGRL, an AUTOmated Graph Representation Learning Framework for node classification. We first design an appropriate search space with four critical components in the automated machine learning (AutoML) pipeline: data augmentation, feature engineering, hyper-parameter optimization, and neural architecture search. Then we search for the best graph representation learning model in the search space on given graph data using an efficient searching algorithm. We conduct extensive experiments on four real-world node classification datasets to demonstrate that AutoG Rlcan automatically find competitive graph representation learning models on specific graph data effectively and efficiently.

Multiplex networks contain multiple types of relations between nodes, where each relation type is modeled as one layer. In the real world, a relation type may only depend on certain attribute features of nodes. Most existing multiplex network embedding methods only focus on preserving consistent information or complementary information from multiplex networks. However, these methods ignore the dependency between node attributes and the topology of each relation. To address the problem, we propose a model called DAME (Disentangled-based Adversarial Network for Multiplex Network Embedding). We utilize generative adversarial learning to preserve the consistent and complementary information between different relation types. Meanwhile, we develop a disentangled graph convolution network (DGCN) based on disentangled learning, enabling DAME to capture the dependency between node attributes and each relation type. We conduct extensive experiments on five realworld datasets. Experimental results indicate the effectiveness of DAME on link prediction and node classification tasks.

Pretraining has proved to be an effective strategy to learn the parameters of the deep neural network.It captures the world knowledge that can be adapted to downstream tasks.Text summarization based on ALBERT [1] outperformed previous work by a large margin.However, they only use the final layer as a contextualized representation of the input text.Multiple studies have proven that intermediate layers also encode the rich hierarchy of linguistic information.In this paper, we propose a Fast Complementary Representation Network (FCRN), which dynamically incorporates linguistic knowledge spread across the entire ALBERT for extractive selection.Different from previous work, we measure the importance of hidden layers by all sentence representations rather than all token embeddings, which can filter nonsignificant words and takes six times less time during training.FCRN first obtains the importance of each layer by sentence embeddings and then automatically absorbs the supplementary information to ALBERT's output.We conduct experiments on CNN/DailyMail and XSum datasets.The results show that our model obtains higher ROUGE scores.

Promotion recommendation, as a new recommendation paradigm in recent years, plays an important role in stimulating the purchase desire of users and maximizing the total revenue. Different from previous recommendations (e.g., item/group recommendation), promotion recommendation aims to select a set of K items based on all user preferences in selection phase and maximize the total revenue in delivery phase. Although these two phases are closely related with each other, existing methods usually focus on item selection in selection phase, largely ignoring the delivery phase and leading to sub-optimal performance. To solve the promotion recommendation problem, we propose the comb-K recommendation model, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints. When selecting K items, the comb-K recommendation is able to simultaneously search the optimal combination of item selection and delivery with the full consideration of all user preferences. Specifically, we propose a novel heterogeneous graph convolutional network to estimate user preference and propose the user-level comb-K recommendation model through solving a binary combination optimization problem. In order to handle combination explosion for large-scale users, we furtherly cluster massive users into limited groups and present a group-level comb-K recommendation model in which a novel heterogeneous graph pooling network is proposed to perform user clustering and estimate group preference. In addition, considering the ”long tail” phenomenon in e-commerce, we design a restricted neighbor heuristic search to accelerate the solving process. Extensive experiments on four datasets demonstrate the superiority of comb-K model for large-scale promotion recommendation. On billion-scale data, when clustering 2.5 × 107 users into 103 groups, our model is able to preserve 98.7% personalized preferences in group-level and significantly improves the Total Click and Hit Ratio by 9.35% and 7.14%, respectively.

Most of existing clustering algorithms are proposed without considering the selection bias in data. In many real applications, however, one cannot guarantee the data is unbiased. Selection bias might bring the unexpected correlation between features and ignoring those unexpected correlations will hurt the performance of clustering algorithms. Therefore, how to remove those unexpected correlations induced by selection bias is extremely important yet largely unexplored for clustering. In this paper, we propose a novel Decorrelation regularized K-Means algorithm (DCKM) for clustering with data selection bias. Specifically, the decorrelation regularizer aims to learn the global sample weights which are capable of balancing the sample distribution, so as to remove unexpected correlations among features. Meanwhile, the learned weights are combined with k-means, which makes the reweighted k-means cluster on the inherent data distribution without unexpected correlation influence. Moreover, we derive the updating rules to effectively infer the parameters in DCKM. Extensive experiments results on real world datasets well demonstrate that our DCKM algorithm achieves significant performance gains, indicating the necessity of removing unexpected feature correlations induced by selection bias when clustering.

Multi-view graph clustering, which seeks a partition of the graph with multiple views that often provide more comprehensive yet complex information, has received considerable attention in recent years. Although some efforts have been made for multi-view graph clustering and achieve decent performances, most of them employ shallow model to deal with the complex relation within multi-view graph, which may seriously restrict the capacity for modeling multi-view graph information. In this paper, we make the first attempt to employ deep learning technique for attributed multi-view graph clustering, and propose a novel task-guided One2Multi graph autoencoder clustering framework. The One2Multi graph autoencoder is able to learn node embeddings by employing one informative graph view and content data to reconstruct multiple graph views. Hence, the shared feature representation of multiple graphs can be well captured. Furthermore, a self-training clustering objective is proposed to iteratively improve the clustering results. By integrating the self-training and autoencoder’s reconstruction into a unified framework, our model can jointly optimize the cluster label assignments and embeddings suitable for graph clustering. Experiments on real-world attributed multi-view graph datasets well validate the effectiveness of our model.

Heterogeneous information network (HIN) contains multiple types of entities and relations. Most of existing HIN embedding methods learn the semantic information based on the heterogeneous structures between different entities, which are implicitly assumed to be complete. However, in real world, it is common that some relations are partially observed due to privacy or other reasons, resulting in a sparse network, in which the structure may be incomplete, and the ”unseen” links may also be positive due to the missing relations in data collection. To address this problem, we propose a novel and principled approach: a Multi-View Adversarial Completion Model (MV-ACM). Each relation space is characterized in a single viewpoint, enabling us to use the topological structural information in each view. Based on the multi-view architecture, an adversarial learning process is utilized to learn the reciprocity (i.e., complementary information) between different relations: In the generator, MV-ACM generates the complementary views by computing the similarity of the semantic representation of the same node in different views; while in the discriminator, MV-ACM discriminates whether the view is complementary by the topological structural similarity. Then we update the node’s semantic representation by aggregating neighborhoods information from the syncretic views. We conduct systematical experiments1 on six real-world networks from varied domains: AMiner, PPI, YouTube, Twitter, Amazon and Alibaba. Empirical results show that MV-ACM significantly outperforms the state-of-the-art approaches for both link prediction and node classification tasks.

Graph has been widely used for modeling complex relationship datasets in different application fields. Social networks based recommendation system have obtained satisfactory results in Business Intelligence(BI). However, current personalized recommendation methods based on graph structure generally lack interactivity and seldom consider efficient data management. To address these problems, Graph OnLine Analytical Mining (GraphOLAM) is a promising method, which combines OLAP technology with social networks. We first propose an efficient recommendation framework based on GraphOLAM data cube technology for the recommendation in the insurance service. Based on this framework, a new algorithm framework named RU-GOLAM for insurance is proposed, which combines GraphOLAM dimensional aggregation operation and specific recommendation methods. A series of graphs can be generated by GraphOLAM dimensional aggregation operations, which reflect the relationships of nodes under the constraints of different hierarchical dimensions. Node similarities are calculated to generate the Top-N sequential recommendation based on all of these graphs, which can achieve the balance between the topology of the original graph and high-dimensional information of the nodes. Experiments show that our approach outperforms other baseline algorithms on an insurance service dataset.

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its meta-path based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.

In the smart power grid, short-term load forecasting (STLF) is a crucial step in scheduling and planning for future load, so as to improve the reliability, cost, and emissions of the power grid. Different from traditional time series forecast, STLF is a more challenging task, because of the complex demand of active and reactive power from numerous categories of electrical loads and the effects of environment. Therefore, we propose NeuCast, a seasonal neural forecasting method, which dynamically models various loads as co-evolving time series in a hidden space, as well as extra weather conditions, in a neural network structure. NeuCast captures seasonality and patterns of the time series by integrating factor modeling and hidden state recognition. NeuCast can also detect anomalies and forecast under different temperature assumptions. Extensive experiments on 134 real-word datasets show the improvements of NeuCast over the stateof-the-art methods.

Distant supervision for relation extraction is an effective method to find novel relational facts from plain text. However, distant supervision inevitably suffers from wrong label problem, and most existing methods generally focus on direct sentences containing entity pairs, but ignore massive information from background knowledge. To tackle these problems, we propose a novel hierarchical attention model to select valid instances and capture vital semantic information in them. Furthermore, we incorporate entity descriptions extracted from Wikipedia into the hierarchical attention model to provide supplementary background knowledge. The proposed architecture can not only combat the noise introduced by distant supervision, but also adequately extract latent and helpful background information. The experimental results on both Chinese and English datasets show that the proposed approach consistently achieves significant improvements on relation extraction as compared with strong baselines.

Abstract In view of the complex relationships involved in network structures, as well as the Markovian nature of information diffusion and the complexity of diffusion paths in social hotspots, we introduce and optimize a basic structure in social networks—the triad. A group behavior diffusion model is proposed based on the optimal triadic structure and factor graph. In addition to predicting individual participation behavior, the model forecasts group development trends of hotspots. First, considering the dynamics of social topic information diffusion, an information triad structure is proposed based on the traditional structure for analyzing the complex influencing factors. Second, in accordance with the Markovian nature of information diffusion, a group behavior diffusion model is proposed associated with the basic concepts and methods of random field theory. This model can not only capture the strengths of different influencing factors but also mine the dynamic patterns of topics. The experimental results reveal the efficiency of our model in both predicting individual participation behavior and perceiving group evolution trends in social hotspots.

In recent years, there is a surge of research on recommender system to alleviate the information overload. Many recommendation techniques have been proposed and they have achieved great successes in many applications. However, the explanation of recommendation results is an important but seldom addressed problem. In this paper, we organize the objects and relations in a recommender system with a heterogeneous information network, which integrates more informations and contains rich semantics. Then we employ a semantic meta path based personalized recommendation model and design a recommender system with explanation, called RecExp. The RecExp system has two unique features. (1) Semantic recommendation. RecExp provides different recommendation models to comply with users' requirements through setting of meta paths. (2) Interpretive recommendation. Under a hybrid recommendation model, RecExp provides the explanations for the recommendation results.

Microblogs play an important role in online social communications. Different from ordinary pieces of information, some hot topics and emerging news will become much more popular in a very short time with the help of this information spreading platform of microblogs. In these "super spreading events", messages are transmitted to a vast range of individuals through a small portion of users engaged in the information diffusion process, a.k.a. super spreaders. Gaining an awareness of super spreading phenomena and an understanding of patterns of vast-ranged information diffusion process is worthy for several tasks such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. In this paper, inspired by the analogous patterns of super spreading in both information diffusion and spread of a contagious disease, we build a parameterized model based on well-known epidemic models to characterize super spreading phenomenon of tweet message diffusion accompanied with super spreaders. Through a fitting process, parameter settings under different scenarios are obtained and the corresponding basic reproduction number is also analyzed, which indicates the degree super spreader will affect the spreading. With the help of the SAIR model, some feasible applications can be exploited.

As a representation of information, Multi-dimension network is more and more popular, such as web data and social network. With the increment of data source, the entities of the network become diverse. How to analyze these multi-dimensional heterogeneous networks effectively and efficiently is a big challenge. In this paper, we propose a Two-Step Multi-dimensional Heterogeneous (TSMH) Graph Cube framework. We use the meta path in heterogeneous network to guide the aggregation of the network and build the Entity Hyper Cube. For the cuboid in Entity Hyper Cube, we do dimension roll-up/drill down to build the Dimension Cube. In Entity Hyper Cube, we design meta path aggregation algorithms and propose materialization strategy. In Dimension Cube, we use hierarchical coding for entities and dimensions and it saves the process of join operations of entities and dimensions which greatly improve the efficiency of dimension operations. In addition, we propose more new Graph OLAP operations which can make network analysis more diverse. At last, we implement the framework in Spark. The results of experiments on real data set and synthetic data set confirm the efficiency and effectiveness of our framework.

We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any information of the target network. Moreover, the source and target networks are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to leverage the heterogeneous interactions between the two networks for the prediction task?

IoT (Internet of Things) bridges the physical world and information space. IoT services are environment sensitive and event-driven. The new IoT service architecture should adapt to these features. This paper analyses IoT sensing service characteristics and proposes the future services architecture. It is focused on the middleware architecture and the interface presentation technology. In the middleware layer, the traditional SOA architecture is insufficient in the real-time response and parallel process of services execution, this paper proposes that the new sensing service system based on EDSOA (Event Driven SOA) architecture to support real-time, event-driven, and active service execution. At presentation layer, this paper presents the new IoT browser features including using augmented reality technology to input and output, and realize the superposition presentation of the physical world and abstract information. Through a use case and proof-of-concept implementation-road manhole covers monitoring system - we verify the feasibility of the proposed ideas and frameworks.

As the microblogging service (such as Weibo) is becoming popular, spam becomes a serious problem of affecting the credibility and readability of Online Social Networks. Most existing studies took use of a set of features to identify spam, but without the consideration of the overlap and dependency among different features. In this study, we investigate the problem of spam detection by analyzing real spam dataset collections of Weibo and propose a novel hybrid model of spammer detection, called SDHM, which utilizing significant features, i.e. user behavior information, online social network attributes and text content characteristics, in an organic way. Experiments on real Weibo dataset demonstrate the power of the proposed hybrid model and the promising performance.

Community detection is one of the most important problems in social network analysis in the context of the structure of the underlying graphs. Many researchers have proposed their own methods for discovering dense regions in social networks. Such methods are only designed with links of the underlying social network. However, with the development of recent applications, rich edge content can be available to give another view to the community detection process. In this study, we focus on improving community detection with the edge content in social networks. In order to regulate the effect of both linkage structure and edge content, we propose two feature integration strategies. Experiment results illustrate that the presence of edge content provides unprecedented opportunities and flexibility for the community detection process.

Detecting communities of complex networks has been an effective way to identify substructures that could correspond to important functions. Conventional approaches usually consider community detection as a single‐objective optimization problem, which may confine the solution to a particular community structure property. Recently, a new community detection paradigm is emerging: multiobjective optimization for community detection, which means simultaneously optimizing multiple criteria and obtaining a set of community partitions. The new paradigm has shown its advantages. However, an important issue is still open: what type of objectives should be optimized to improve the performance of multiobjective community detection? To exploit this issue, we first proposed a general multiobjective community detection solution (called NSGA‐Net) and then analyzed the structural characteristics of communities identified by a variety of objective functions that have been used or can potentially be used for community detection. After that, we exploited correlation relations (i.e., positively correlated, independent, or negatively correlated) between any two objective functions. Extensive experiments on both artificial and real networks demonstrate that NSGA‐Net optimizing over a pair of negatively correlated objectives usually leads to better performances compared with the single‐objective algorithm optimizing over either of the original objectives, or even to other well‐established community detection approaches.

Currently the increasing prevalence of multi-homing and traffic engineering leads to an explosive growth of the global routing table. It is well known that hierarchical addressing could improve the routing scalability. Hence, some proposals exploring the routing architecture for future Internet reuse the hierarchical addressing for the locator assignment. However, this may result in some ASes assigned too many prefixes which would consequently make hosts, routers, Internet Service Providers and Domain Name System faced with big challenges. By modeling the Internet AS-level topology using a hierarchical graph, we define the processes of prefix assignment and routing advertisement in different hierarchical addressing deployment ways. Then, we quantify the impact of these deployments on the prefix assignment and the routing scalability based on the real routing data. we find that when the deploying position gets lower, the prefix amount of arbitrary AS is getting smaller, while the size of the global Forwarding Information Base is monotonically increasing. Comparing with the actual Internet's data, suitable deployment ways for hierarchical addressing are obtained. With these deployment ways, the excessive prefix problem is solved and the size of the global Forwarding Information Base could be reduced into 56% or even 32% of the one in current Internet.

Complex networks have attracted much research attentions. Community detection is an important problem in complex network which is useful in a variety of applications such as information propagation, link prediction, recommendations and marketing. In this paper, we focus on discovering overlapping community structure using link partition. We proposed a LDA-based link partition (LBLP) method which can find communities with adjustable range of overlapping. This method employs topic model to detect link partition, which can calculate the community belonging factor for each link. Based on the belonging factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of our solution on both real-world and synthesized networks. The experiment results demonstrate that the approach can find meaningful and relevant link community structure.

Making accurate recommendations for users has become an important function of e-commerce system with the rapid growth of WWW. Conventional recommendation systems usually recommend similar objects, which are of the same type with the query object without exploring the semantics of different similarity measures. In this paper, we organize objects in the recommendation system as a heterogeneous network. Through employing a path-based relevance measure to evaluate the relatedness between any-typed objects and capture the subtle semantic containing in each path, we implement a prototype system (called HeteRecom) for semantic based recommendation. HeteRecom has the following unique properties: (1) It provides the semantic-based recommendation function according to the path specified by users. (2) It recommends the similar objects of the same type as well as related objects of different types. We demonstrate the effectiveness of our system with a real-world movie data set.

Telecom BI(Business Intelligence) system consists of a set of application programs and technologies for gathering, storing, analyzing and providing access to data, which contribute to manage business information and make decision precisely. However, traditional analysis algorithms meet new challenges as the continued exponential growth in both the volume and the complexity of telecom data. With the Cloud Computing development, some parallel data analysis systems have been emerging. However, existing systems have rarely comprehensive function, either providing data analysis service or providing social network analysis. We need a comprehensive tool to store and analysis large scale data efficiently. In response to the challenge, the SaaS (Software-as-a-Service) BI system, BC-PDM (Big Cloud-Parallel Data Mining), are proposed. BC-PDM supports parallel ETL process, statistical analysis, data mining, text mining and social network analysis which are based on Hadoop. This demo introduces three tasks: business recommendation, customer community detection and user preference classification by employing a real telecom data set. Experimental results show BC-PDM is very efficient and effective for intelligence data analysis.

Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on the single objective setting, where the learning algorithm optimizes over a single performance criterion (e.g. Ranking Loss) or a heuristic function. The basic assumption is that the optimization over one single objective can improve the overall performance of multilabel classification and meet the requirements of various applications. However, in many real applications, an optimal multi-label classifier may need to consider the tradeoffs among multiple conflicting objectives, such as minimizing Hamming Loss and maximizing Micro F1. In this paper, we study the problem of multi-objective multi-label classification and propose a novel solution (called M oml) to optimize over multiple objectives simultaneously. Note that optimization objectives may be conflicting, thus one cannot identify a single solution that is optimal on all objectives. Our M oml algorithm finds a set of non-dominated solutions which are optimal according to the different tradeoffs of the multiple objectives. So users can flexibly construct various combined predictive models from the solution set, which helps to provide more meaningful classification results in different application scenarios. Empirical studies on real-world tasks demonstrate that the M oml can effectively boost the overall performance of multi-label classification, not limiting to the optimization objectives.

Link prediction in complex networks is an important issue in graph mining. It aims at estimating the likelihood of the existence of links between nodes by the know network structure information. Currently, most link prediction algorithms based on local information consider only the individual characteristics of common neighbors. In this paper, first, we study the link prediction results as the change of the exponent on the degree of common neighbors, and find some regular pattern between different networks and different exponent. After that, we come up with a new algorithm exploiting the interactions between common neighbors, namely Individual Attraction Index. To reduce the time complexity, we design a simple edition, called Simple Individual Attraction Index. We compare nine well-known local information metrics on eight real networks. The result proves well the best overall performance of these two new algorithms.

Most of the existing literature which has entirely focused on clustering nodes in large-scale networks. To discover multi-scale overlapping communities quickly, we propose a highly efficient multi-resolution link community detection algorithm to detect the link communities in massive networks based on the idea of edge labeling. First, we will get the node partition of the network based on a new multi-resolution node detection algorithm. After that, we can find the link community in a linear time by the labels of nodes. Its time complexity is near linear and its space complexity is linear. The effectiveness of our algorithm is demonstrated by extensive experiments on lots of computer generated artificial graphs and real-world networks. The results show that our algorithm is very fast and highly reliable. Tests on real and artificial networks also give excellent results comparing with the newly proposed link partition algorithm.

Most of trustworthy web service selection simply focus on individual reputation and ignore the collaboration reputation between services. To enhance the collaboration trust during web service selection, a reputation model called collaboration reputation is proposed. The reputation model is built on web service collaboration network(WSCN), which is constructed in terms of the composite service execution log. Thus, the WSCN aims to maintain the trustworthy collaboration alliance among web services, In WSCN, the collaboration reputation can be assessed by two metrics, one called invoking reputation is computed by recommendation, which is selected from the community structure hiding in WSCN, the other is assessed by the invoked web service. In addition, the web service selection based on WSCN is designed.

In order to promote efficiency of electronic business operating, the newly founded three Telecom operators in China began to reconstruct their CRM of OSS (Operation Support Systems) which offering full telecom service including Fixed and Mobile telephone services. Aiming to support continuous release of new telecom products and services, it is necessary for Telecom operators to offer flexible combination and separation of telecom business in the reconstruction of Telecom CRM among distributed heterogeneous OSS. A novel service atomization method has been proposed in this paper which foucus on resource modeling and service evaluating based on SOA and workflow technology to decrease maintenance complexity of business logic code from Telecom CRM. Experiments demonstrated the effectiveness of the proposed method in performance promotion basing on the real CRM reconstruction project in province branch of China Unicom.

To efficiently support customers to find web service which satisfies the non-functional requirement, a distributed quality of service (QoS) index framework is proposed in this paper. The framework consists of two kinds of structured distributed system, one is chord overlay network which is assigned to classify the web services according to the similarity of web services descriptions, the other is a tree-structured distributed network named DQoS tree which manages the classified web services' QoS. Since there is a dominance relation among multi-dimension QoS, therefore, a guideline based on this relation to construct a QoS tree managed by a sub peer is designed. Through the dominance relation, it's easily find the qualified QoS among web services with less compare times, thus, the query effectiveness is guaranteed. Additionally, to avoid the load imbalance of query load in the DQoS tree, a load balance strategy is introduced which can balance work load efficiently. Experimental results validate the effectiveness and efficiency of the proposed approaches.

Currently, community detection has led to a huge interest in data analysis on real-world networks. However, the high computationally demanding of most community detection algorithms limits their applications. In this paper, we propose a heuristic algorithm to extract the community structure in large networks based on local community attractive force optimization whose time complexity is near linear and space complexity is linear. The effectiveness of our algorithm is demonstrated by extensive experiments on lots of computer generated graphs and public available real-world graphs. The result shows our algorithm is extremely fast, and it is easy for us to explore massive networks interactively.

Recently, a growing number of researches have focused on the issues raised by the knowledge discovery of online information, particularly the problems of tracking topics, ideas, and users' spreading influence across the Web. In this paper, the search-engine query logs on Topic Detection and Tracking (TDT) is analyzed other than study of the quality of the search result or query recommendation. By constructing a novel bi-type heterogeneous query graph, the queries' semantic similarity and query-URL relation are combined together. Utilizing social network analysis (SNA) method to analyze the query graph with optimization of the community discovery algorithm LPA by grouping the nodes who are linked with the same URL initially, we can find the topics in the query logs. To evaluate the topic evolution pattern, we group the similar communities over each adjacent time stamps into clusters. Extensive experiments demonstrate the effectiveness and efficiency of the methods.

With the emergence of massive social media, massive social networks have led to a huge interest in data analysis. In this paper, we propose an empirical study on several massive social networks including 4 mobile call graphs, a fixed-line call graph, two co-authorship networks and two Email networks. We find that call graphs tend to be more locality than the co-authorship networks and Email networks. To our surprise, we even find that there is no significant relations between community sizes and their quality scores for most extracted communities. We also find that some very huge community with high mean quality values, and we can not find the universal "V" shape in their mean quality values.

Given a real, and weighted person-to-person network which changes over time, what can we say about the cliques that it contains? Do the incidents of communication, or weights on the edges of a clique follow any pattern? Real, and in-person social networks have many more triangles than chance would dictate. As it turns out, there are many more cliques than one would expect, in surprising patterns.

Many systems in nature and human society take the form of networks with community structures. In this paper, we describe a simple algorithm COCD(Clique-based Overlapping Community Detection) to efficiently mine the overlapping communities in large-scale networks, which is useful for us to have a better understanding of the nested sub-structures embedded in the whole network.

Researches have discovered that rich interactions among entities in nature and human society bring about complex networks with community structures. In this paper, we propose a novel algorithm BiTector (bi-community detector) to mine the overlapping communities in large-scale sparse bipartite networks. We apply the algorithm to various real-world datasets, showing that BiTector can identify the overlapping community structures in the bipartite networks efficiently and effectively.

Recent years have seen a thriving development of the World Wide Web as the most visible social media which enables people to share opinions, experiences and expertise with each other across the world. People now get involved in many different social networks simultaneously, which are often large intricate web of connections among the massive entities they are made of. As a result, the challenge of collecting and analyzing large-scale data among social members has left most basic questions about the global composition and function of such networks largely unresolved: What is the essential organization of a social network? who are the influential individuals whose voice is echoed by others? To address these questions, this paper presents an algorithm called sketcher to discover and describe the overall backbone of a specific network. Experimental results on the American College Football, Scientific Collaboration, and Telecommunications Call networks show that sketcher can extract the essential composition of a social network both efficiently and intuitively.

Grid information service (GIS) provides the function of discovering and monitoring resources, which is the foundation of the grid infrastructure. It is important to ensure its reliability and efficiency. This paper proposes a novel information service architecture based on group of interest (GOI) mechanism, which is completely distributed and has high scalability and reliability. In a GOI, all GIS nodes have the uniform view of information by exchanging their information periodically for efficient service discovery and fault-tolerance. A super GIS node is selected in each GOI to take full responsibility for the queries submitted to this GOI. This mechanism can reduce the query distance and enhance the query efficiency. Experimental results show that the mechanism satisfies the expectations