Graph neural networks (GNNs) have been extensively utilized in various fields due to their efficient and strong capability in processing graph-structured data. However, as the scale of real-world graphs continues to grow, processing large-scale graphs with GNNs has become increasingly storage-intensive and time-consuming. Graph condensation, which aims to synthesize a smaller yet informative substitute graph, has emerged as a promising solution. Most existing approaches, however, primarily focus on feature condensation while paying less attention to faithfully reconstructing structural information. Moreover, the condensed representations are typically dense, which not only limits storage and computational efficiency but also risks retaining noise and redundancy. To address these limitations, we propose DGC, a Discrete Graph Condensation framework, where numerous nodes in the original graph can be represented by the discrete codes in the codebook while maintaining the original structure information. Specifically, we leverage high-order proximity between nodes to capture the original graph's structure and transfer it to the condensed graph via low-rank approximation. Meanwhile, node features are discretized and compressed through a vector quantized variational autoencoder, effectively reducing redundancy and enhancing storage efficiency while retaining critical information. Extensive experiments on multiple real-world graphs demonstrate the effectiveness of our proposed model against nine competitive baseline methods across different condensation ratios.

In recent years, Message Passing Neural Networks (MPNNs) have emerged as a leading paradigm for learning over graph-structured data, owing to their simple architecture and excellent scalability. However, their expressive power is strictly limited by the one-dimensional Weisfeiler–Leman (1-WL) test. A common approach to enhance expressivity is designing models based on higher-order k -WL and k -Folklore WL ( k -FWL) tests, collectively referred to as tuple-based GNNs. Despite their greater expressive power, these methods face two major challenges: (1) The neighboring tuples in k -WL/FWL are generated by replacing any node in the graph, disregarding adjacency relationships and thus failing to capture structural dependencies between tuples. (2) The design of k -WL/FWL lacks flexibility, while increasing k enhances expressivity, it also results in an exponential surge in time and space complexity. To overcome these limitations, we propose Trail-WL, a more expressive variant of the WL test, along with its neural network counterpart, Trail-based Tuple Neural Network (TTNN). We redefine node tuples as collections of trail structures, providing a more intuitive representation of graph connectivity compared to prior tuple-based methods. By incorporating variable trail lengths and node distance constraints, our model adaptively balances expressivity and scalability while preserving essential structural features. Furthermore, we propose and formally prove three theoretical expressivity theorems for Trail-WL. Finally, we conduct comprehensive experiments on both synthetic and real-world datasets, demonstrating that our approach consistently matches or outperforms baseline methods, thereby substantiating its effectiveness.

Long-term time series forecasting has been widely studied, yet two aspects remain insufficiently explored: the interaction learning between different frequency components and the exploitation of periodic characteristics inherent in timestamps. To address the above issues, we propose CFPT, a novel method that empowering time series forecasting through Cross-Frequency Interaction (CFI) and Periodic-Aware Timestamp Modeling (PTM). To learn cross-frequency interactions, we design the CFI branch to process signals in frequency domain and captures their interactions through a feature fusion mechanism. Furthermore, to enhance prediction performance by leveraging timestamp periodicity, we develop the PTM branch which transforms timestamp sequences into 2D periodic tensors and utilizes 2D convolution to capture both intra-period dependencies and inter-period correlations of time series based on timestamp patterns. Extensive experiments on multiple real-world benchmarks demonstrate that CFPT achieves state-of-the-art performance in long-term forecasting tasks. The code is publicly available at this repository: https://github.com/BUPT-SN/CFPT.

The advancements in generative models and the real-world attack of machine-generated text(MGT) create a demand for more robust detection methods. The existing MGT detection methods for adversarial environments primarily consist of manually designed statistical-based methods and fine-tuned classifier-based approaches. Statistical-based methods extract intrinsic features but suffer from rigid decision boundaries vulnerable to adaptive attacks, while fine-tuned classifiers achieve outstanding performance at the cost of overfitting to superficial textual feature. We argue that the key to detection in current adversarial environments lies in how to extract intrinsic invariant features and ensure that the classifier possesses dynamic adaptability. In that case, we propose OSTAR, a novel MGT detection framework designed for adversarial environments which composed of a statistical enhanced classifier and a Multi-Faceted Contrastive Learning(MFCL). In the classifier aspect, our Multi-Dimensional Statistical Profiling (MDSP) module extracts intrinsic difference between human and machine texts, complementing classifiers with useful stable features. In the model optimization aspect, the MFCL strategy enhances robustness by contrasting feature variations before and after text attacks, jointly optimizing statistical feature mapping and baseline pre-trained models. Experimental results on three public datasets under various adversarial scenarios demonstrate that our framework outperforms existing MGT detection methods, achieving state-of-the-art performance and robust against attacks.The code is available at https://github.com/BUPT-SN/OSTAR.

User profiling has very important applications for many downstream tasks. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Fortunately, we observed that similar users have similar behavior patterns in different social networks. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design two efficient frameworks for User Profile transferring acrOss Networks, i.e., UPON and E-UPON. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model to find user dependencies within and between domains (i.e., social networks). We then design a dual-domain weighted adversarial learning method to address the domain shift problem existing in the transferring procedure. In E-UPON, we optimize UPON in terms of computational complexity and memory. Specifically, we design a mini-cluster gradient descent based graph representation algorithm to shrink the searching space and ensure parallel computation. Then we use an adaptive cluster matching method to adjust the clusters of users. Experimental results on Twitter-Foursquare dataset demonstrate that UPON and E-UPON outperform the state-of-the-art models.

Relation triple extraction is an essential task in information extraction and knowledge graph construction. Despite recent advancements, existing methods still exhibit certain limitations. Firstly, they only employ generalized pre-trained models and do not consider the specificity of relation triple extraction tasks. Moreover, existing tagging-based approaches typically decompose the task into two subtasks, initially identifying subjects and subsequently identifying objects and relations. They solely focus on extracting relational triples from subject→object, neglecting that once the extraction of a subject fails, it fails in extracting all triples. To address these issues, we propose SuperLED, an innovative supervised contrastive learning based dual path triple extraction framework. Specifically, we design a supervised contrastive learning method that considers multiple positive samples per anchor rather than restricting it to only one positive. Furthermore, a penalty term is introduced to prevent excessive similarity between the subject and object. Our framework implements taggers in two paths, enabling triples extraction from subject→object and object→subject. Experimental results show that SuperLED achieves state-of-the-art results on the benchmark datasets and significantly improves the F1 score on Normal, SEO, EPO, and multiple relation extraction tasks.

Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual influence between users and items/products. Efforts on representing SIN are mainly focused on capturing the dynamics of networks in Euclidean space, and recently plenty of work has extended to hyperbolic geometry for implicit hierarchical learning. Previous approaches which learn the embedding trajectories of users and items achieve promising results. However, there are still a range of fundamental issues remaining open. For example, is it appropriate to place user and item nodes in one identical space regardless of their inherent discrepancy? Instead of residing in a single fixed curvature space, how will the representation spaces evolve when new interaction occurs?

Network alignment aims to discover nodes in different networks belonging to the same identity. In recent years, the network alignment problem has aroused significant attentions in both industry and academia. With the rapid growth of information, the sizes of networks are usually very large and in most cases we only focus on the alignment of partial networks. However, under this circumstances, the collected network data may be highly biased, and the training and testing data are no longer i.i.d. (identically and independently distributed). Thus, it is difficult for the trained alignment model to have a good performance in the test set. To bridge this gap, in this paper, we propose a novelDomain generAlization based netWork aligNment approach termed as DAWN. Specifically, in DAWN, we first design a novel invariant feature extraction model which leverages adversarial learning to extract domain-invariant features. Then, we design a novel invariant network alignment model which can achieve global optimum and local optimum simultaneously to learn domain-invariant alignment patterns. Finally, we conduct extensive experiments on the benchmark dataset of Facebook-Twitter, and results show that DAWN can averagely achieve 14.01% higher Hits@k and 10.63% higher MRR@k compared with the state-of-the-art methods.

Social network alignment, identifying social accounts of the same individual across different social networks, shows fundamental importance in a wide spectrum of applications, such as link prediction and information diffusion. Individuals more often than not join in multiple social networks, and it is in fact much too expensive or even impossible to acquiring supervision for guiding the alignment. To the best of our knowledge, few method in the literature can align multiple social networks without supervision. In this article, we propose to study the problem of unsupervised multiple social network alignment. To address this problem, we propose a novel unsupervised model of joint Matrix factorization with a diagonal Cone under orthogonal Constraint, referred to as MC 2 . Its core idea is to embed and align multiple social networks in the common subspace via an unsupervised approach. Specifically, in MC 2 model, we first design a matrix optimization to infer the common subspace from different social networks. To address the nonconvex optimization, we then design an efficient alternating algorithm by leveraging its inherent functional property. Through extensive experiments on real-world datasets, we demonstrate that the proposed MC 2 model significantly outperforms the state-of-the-art methods.

Social network alignment, aligning different social networks on their common users, is receiving dramatic attentions from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic Graph autoencoder based dynamic social network Alignment approach, referred to as DGA, unfolding the fruitful dynamics of social networks for user alignment. However, it faces challenges in both modeling and optimization: (1) To model the intra-network dynamics, we design a novel dynamic graph autoencoder to learn user embeddings with complex network dynamics. (2) To model the inter-network alignment, we design a unified optimization framework over proposed dynamic graph autoencoders, constructing a common subspace for user alignment across different networks. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed approach substantially outperforms the state-of-the-art methods.

Network alignment aims to discover nodes in different networks belonging to the same identity. In recent years, the network alignment problem has aroused significant attentions in both industry and academia. However, the continuous exploding of network data brings two challenges in solving the network alignment problem, i.e., large network scale and scarce labeled data. To bridge this gap, in this paper we propose a novel approach termed asMulti-granularItyNetwork alIgnment based on coNtrastive learninG(MINING). Specifically, in MINING, we first design multi-granularity alignment framework to solve the issue of large network scale. Then, we design intra- and inter-network contrastive learning to solve the issue of scarce labeled data. Moreover, we provide theoretical proofs to demonstrate the effectiveness of MINING. Finally, we conduct extensive experiments on the benchmark datasets of Facebook-Twitter, AMiner-LinkedIn and DBpedia$_{\text{ZH}}$-DBpedia$_{\text{EN}}$, and results show that MINING can averagely achieve 15.93% higher$\operatorname{Hits@}k$and 14.82% higher$\operatorname{MRR@}k$compared with the state-of-the-art methods.

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users' behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment and user behavior analysis problem in this paper. We design a novel framework named BANANA-RGB. In this framework, to capture users' multi-scale behavior information in each social network, we train a variant of the hierarchical periodic memory network with personalized memorization. To leverage behavior analysis for social network alignment, we design a tensor fusion network-based alignment component to improve the performance. To further leverage social network alignment for behavior analysis, we design a gating-based cross-network behavior fusion component to integrate users' behavior information in different social networks based on the alignment result. We iteratively train the above two components to make the two tasks benefit from each other. Extensive experiments on real-world datasets demonstrate that our proposed approach outperforms the state-of-the-art methods.

Graph representation learning received increasing attentions in recent years. Most of the existing methods ignore the complexity of the graph structures and restrict graphs in a single constant-curvature representation space, which is only suitable to particular kinds of graph structure indeed. Additionally, these methods follow the supervised or semi-supervised learning paradigm, and thereby notably limit their deployment on the unlabeled graphs in real applications. To address these aforementioned limitations, we take the first attempt to study the self-supervised graph representation learning in the mixed-curvature spaces. In this paper, we present a novel Self-Supervised Mixed-Curvature Graph Neural Network (SelfMGNN). To capture the complex graph structures, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces, and design hierarchical attention mechanisms for learning and fusing graph representations across these component spaces. To enable the self-supervised learning, we propose a novel dual contrastive approach. The constructed mixed-curvature space actually provides multiple Riemannian views for the contrastive learning. We introduce a Riemannian projector to reveal these views, and utilize a well-designed Riemannian discriminator for the single-view and cross-view contrastive learning within and across the Riemannian views. Finally, extensive experiments show that SelfMGNN captures the complex graph structures and outperforms state-of-the-art baselines.

As an essential component of task-oriented dialogue systems, slot filling requires enormous labeled training data in a certain domain.However, in most cases, there is little or no target domain training data is available in the training stage.Thus, cross-domain slot filling has to cope with the data scarcity problem by zero/few-shot learning.Previous researches on zero/few-shot cross-domain slot filling focus on slot descriptions and examples while ignoring the slot type ambiguity and example ambiguity issues.To address these problems, we propose Abundant Information Slot Filling Generator (AISFG), a generative model with a novel query template that incorporates domain descriptions, slot descriptions, and examples with context.Experimental results show that our model outperforms state-of-the-art approaches in zero/few-shot slot filling task. 1

Knowledge graph embedding aims to learn representations of entities and relations in low-dimensional space. Recently, extensive studies combine the characteristics of knowledge graphs with different geometric spaces, including Euclidean space, complex space, hyperbolic space and others, which achieves significant progress in representation learning. However, existing methods are subject to at least one of the following limitations: 1) ignoring the uncertainty, 2) incapability of complex relation patterns. To address the above issues simultaneously, we propose a novel model named DiriE, which embeds entities as Dirichlet distributions and relations as multinomial distributions. DiriE employs Bayesian inference to measure the relations between entities and learns binary embeddings of knowledge graphs for modeling complex relation patterns. Additionally, we propose a two-step negative triple generation method that generates negative triples of both entities and relations. We conduct a solid theoretical analysis to demonstrate the effectiveness and robustness of our method, including the expressiveness of complex relation patterns and the ability to model uncertainty. Furthermore, extensive experiments show that our method outperforms state-of-the-art methods in link prediction on benchmark datasets.

In network virtualization, one of the key problems is to embed a sequence of virtual networks with both node and link constraints onto the physical network, which is known to be NP-hard. Recent studies focus on how to minimize the energy cost while maximizing the revenue of the physical network when the VN request arrives. However, after a period of time, due to the dramatic dynamics of the resources of the physical network, the previous solution may become less energy efficient. In this article, we study how to re-optimize the energy cost by leveraging the migration technique. In particular, we first give the problem model of virtual network migration. Then we design two energy aware virtual network migration algorithms called EA-VNM and EA-VNM-G. For EA-VNM, it answers the following key questions: when to perform migration, migrate which virtual nodes to where, and how to perform migration. Especially, for EA-VNM-G, it further reduces the high time complexity problem of EA-VNM by grouping the virtual nodes to be migrated with fewer conflicts. Extensive simulations show that EA-VNM significantly reduces the energy cost by up to 25 percent over the state-of-the-art algorithm while maintaining similar revenue and EA-VNM-G reduces the running time significantly.

In social networks, one of the most significant challenges is how to estimate the tweet popularity. Prior studies focus on leveraging different aspects of just a single tweet, while ignoring the impact of historical tweets. In this article, we propose to leverage such historical information and rethink the problem of tweet popularity estimation. From historical information, there are two important factors that can be extracted: (1) user reputation feature, which can represent coarse-grained level of tweet popularity and (2) tweet related features, which can represent fine-grained level of tweet popularity. To incorporate these two factors from historical information, we design a novel deep neural architecture, a Bi-layered LSTM for tweet popUlarity Estimation, called DeepBlue. Specifically, we first propose a user-reputation aware mechanism to combine coarse-grained and fine-grained level estimation into a united LSTM model. We also design a content attention mechanism to consider different impacts of historical tweets in terms of content similarity. We then propose a time aware mechanism to address the time interval irregularity issue. Finally, we apply the Poisson regression model to obtain the overall loss for tweet popularity estimation. Extensive experiments demonstrate the superiority of our proposed approach to other state-of-the-arts in terms of MAE and SRC.

Learning representations for graphs plays a critical role in a wide spectrum of downstream applications. In this paper, we summarize the limitations of the prior works in three folds: representation space, modeling dynamics and modeling uncertainty. To bridge this gap, we propose to learn dynamic graph representations in hyperbolic space, for the first time, which aims to infer stochastic node representations. Working with hyperbolic space, we present a novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on a theoretically grounded time encoding approach. To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions. Furthermore, we introduce a reparameterisable sampling algorithm for the hyperbolic normal distribution to enable the gradient-based learning of HVGNN. Extensive experiments show that HVGNN outperforms state-of-the-art baselines on real-world datasets.

User profiling is playing an increasingly important role in real-world applications. Previous works have shown that integrating user information from multiple social networks helps to significantly improve the performance of user profiling. However, these studies either ignore the different contributions of various features in different profiling tasks or need to train one model for each task. What’s more, the assumption of the strong relatedness between user profiling tasks limits their application. These phenomena make inferring comprehensive user attributes still an open problem. In this paper, we propose a novel method, called Hierarchical Attention-based Model with sparse-sharing-based muLti-task sElf-Training algorithm (HAMLET), for comprehensive user profiling. More specifically, we first employ a hierarchical attention-based network as our base network to represent users. It assigns various features from different social networks with different weights for different users during the fusing procedure. Then, we propose a multi-task self-training algorithm that takes advantage of both task correlations and self-training to obtain better performance. We conduct extensive experiments on two real-world datasets and verify the superiority of HAMLET for user profiling.

Recently, reconciling social networks, identifying the accounts belonging to the same individual across social networks, receives significant attention from both academic and industry. Most of the existing studies have limitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address these limitations, we rethink this problem and, for the first time, robustly and comprehensively reconcile multiple social networks. In this paper, we propose two frameworks, MASTER and MASTER+, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In MASTER, we first design a novel Constrained Dual Embedding model, simultaneously embedding and reconciling multiple social networks, to formulate this problem into a unified optimization. To address this optimization, we then design an effective NS-Alternating algorithm and prove it converges to KKT points. To further speed up MASTER, we propose a scalable framework, namely MASTER+. The core idea is to group accounts into clusters and then perform MASTER in each cluster in parallel. Specifically, we design an efficient Augmented Pre-Embedding model and Balance-aware Fuzzy Clustering algorithm for the high efficiency and the high accuracy. Extensive experiments demonstrate that both MASTER and MASTER+ outperform the state-of-the-art approaches. Moreover, MASTER+ inherits the effectiveness of MASTER and enjoys higher efficiency.

User profiling has very important applications for many downstream tasks, such as recommender system, behavior prediction and market strategy. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Modeling user profiles under such conditions often leads to poor performance. Fortunately, we observed that not only user attributes but also user relationships are useful for user profiling and benefit the results. Meanwhile, similar users have similar behavior in different social networks. Finding user dependencies between social networks will help to infer user profiles. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design an efficient User Profile transferring acrOss Networks (UPON) framework, which transfers knowledge of user relationship from one social network with plenty of data to facilitate the user profiling on the other social network with scarce data. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model (GCN-CDAM) to find user dependencies within and between domains (referring to social networks). We then design a dual-domain weighted adversarial learning method to solve the domain shift problem existing in the transferring procedure. Experimental results on Twitter-Foursquare dataset demonstrate that UPON outperforms the state-of-the-art models.

With the growing prosperity and diversity of social networks, more and more users participate in multiple social networks to enjoy their diverse services. Users can create different user identities in different social networks, but most networks are independent of each other. Hence, a fundamental question arises: can we align user identities across social networks? Related work mostly focuses on Euclidean geometry to learn representation vectors of social networks. However, motivated by recent advances in geometry representation learning, we find that hyperbolic geometry shows the advantage of expressing network hierarchical structure, while Euclidean geometry doesn't. Thus, in this paper, we first introduce the connection between hyperbolic space and social networks. Then we propose a novel hyperbolic geometry representation learning model for user identity linkage across social networks, which is called “HUIL”. Finally, we conduct comprehensive experiments on real-world datasets and verify the superiority of HUIL for user identity linkage.

Social network alignment shows fundamental importance in a wide spectrum of applications. To the best of our knowledge, existing studies mainly focus on network alignment at the individual user level, requiring abundant common information between shared individual users. For the networks that cannot meet such requirements, social community structures actually provide complementary and critical information at a slightly coarse-grained level, alignment of which will provide additional information for user alignment. In turn, user alignment also reveals more clues for community alignment. Hence, in this paper, we introduce the problem of joint social network alignment, which aims to align users and communities across social networks simultaneously. Key challenges lie in that (1) how to learn the representations of both users and communities, and (2) how to make user alignment and community alignment benefit from each other. To address these challenges, we first elaborate on the characteristics of real-world networks with the notion of δ-hyperbolicity, and show the superiority of hyperbolic space for representing social networks. Then, we present a novel hyperbolic embedding approach for the joint social network alignment, referred to as Perfect, in a unified optimization. Extensive experiments on real-world datasets show the superiority of Perfect in both user alignment and community alignment.

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.

Social network alignment, identifying social accounts of the same individual across different social networks, shows fundamental importance across a wide spectrum of applications. Individuals more often than not join in multiple social networks and it is in fact intractable or even impossible to acquiring supervision for guiding the alignment. However, to the best of our knowledge, none of existing methods can align multiple social networks without supervision. In this paper, we propose to study the problem of unsupervised multiple social network alignment. To address this problem, we propose a novel unsupervised model of Matrix factorization with diagonal Cone under orthogonal Constraint, referred to as MC2. Its core idea is to embed and align multiple social networks in the common subspace via an unsupervised approach. Specifically, in MC2model, we first design a matrix optimization to infer the common subspace from different social networks. To address the nonconvex optimization, we then design an efficient alternating algorithm by leveraging its inherent functional property. Through extensive experiments on real-world datasets, we demonstrate that the proposed MC2model significantly outperforms the state-of-the-art methods.

Social network alignment, aligning different social networks on their common users, is receiving dramatic attention from both academic and industry. All existing studies consider the social network to be static and neglect its inherent dynamics. In fact, the dynamics of social networks contain the discriminative pattern of an individual, which can be leveraged to facilitate social network alignment. Hence, we for the first time propose to study the problem of aligning dynamic social networks. Towards this end, we propose a novel Dynamic social Network Alignment (DNA) framework, a unified optimization approach over deep neural architectures, to unfold the fruitful dynamics to perform alignment. However, it faces tremendous challenges in both modeling and optimization: (1) To model the intra-network dynamics, we explore the local dynamics of the latent pattern in friending evolvement and the global consistency of the representation similarity with neighbors. We design a novel deep neural architecture to obtain the dual embedding capturing local dynamics and global consistency for each user. (2) To model the inter-network alignment, we exploit the underlying identity of an individual from the dual embedding in each dynamic social network. We design a unified optimization approach interplaying proposed deep neural architectures to construct a common subspace of identity embeddings. (3) To address this optimization problem, we design an effective alternating algorithm with solid theoretical guarantees. We conduct extensive experiments on real-world datasets and show that the proposed DNA framework substantially outperforms the state-of-the-art methods.

In social networks, one of the most significant challenges is how to estimate the tweet popularity. Prior studies about this problem focus on leveraging different aspects of just a single tweet, while ignoring the impact of historical tweets. In this paper, we propose to leverage such historical information and rethink the problem of tweet popularity estimation. From historical information, there are two important factors that can be extracted: (1) user reputation feature, which can represent coarse-grained level of tweet popularity; (2) tweet related features, which can represent fine-grained level of tweet popularity. To incorporate two factors from historical information, we design a novel deep neural architecture, a Bi-layered LSTM for tweet popularity Estimation, called DeepBlue. Specifically, we first propose a user-reputation aware mechanism to combine coarse-grained and fine-grained level estimation into a unified LSTM model. We then propose a time aware mechanism to address the time interval irregularity issue in standard LSTM. Finally, we apply the Poisson regression model to obtain the overall loss for tweet popularity estimation. Extensive experiments demonstrate the superiority of our proposed approach to other state-of-the-arts in terms of MAE and SRC.

Supervised topic classifiers for Twitter and other media sources are important in a variety of long-term topic tracking tasks. Unfortunately, over long periods of time, features that are predictive during the training period may prove ephemeral and fail to generalize to prediction at future times. For example, if we trained a classifier to identify tweets concerning the topic of “Celebrity Death”, individual celebrity names and terms associated with these celebrities such as “Nelson Mandela” or “South Africa” would prove to be temporally unstable since they would not generalize over long periods of time; in contrast, terms like “RIP” (rest in peace) would prove to be temporally stable predictors of this topic over long periods of time. In this paper, we aim to design supervised learning methods for Twitter topic classifiers that are capable of automatically downweighting temporally unstable features to improve future generalization. To do this, we first begin with an oracular approach that chooses temporally stable features based on knowledge of both train and test data labels. We then search for feature metrics evaluated on only the training data that are capable of recovering the temporally stable features identified by our oracular definition. We next embed the top-performing metric as a temporal stability regularizer in logistic regression with the important property that the overall training objective retains convexity, hence enabling a globally optimal solution. Finally, we train our topic classifiers on 6 Twitter topics over roughly one year of data and evaluate on the following year of data, showing that logistic regression with our temporal stability regularizer generally outperforms logistic regression without such regularization across the full precision-recall continuum. Overall, these results establish a novel regularizer for training long-term temporally stable topic classifiers for Twitter and beyond.

In the past few years, there has been a rapid growth in cloud computing, especially IaaS. It's a trend to use hybrid clouds that incorporate components of private and public clouds in order to achieve lower cost, higher availability and better security. Enterprises could get the benefits from it, including high elasticity, efficiency, flexibility, etc. In the meantime, they are faced with a set of challenges in cloud management. However, previous researches have primarily focused on single cloud platform, with lack of efficient management of hybrid clouds. To bridge this gap, in this paper, we propose a distributed hybrid cloud management platform based on rule engine. We design a succinct rule model to define rules and events. Combined with a distributed and reliable architecture, the demands of batch processing, automatic operation could be met, which make it more flexible and efficient to manage cloud resources from multiple IaaS cloud providers.

In recent years, friending recommendation has played a crucial role on the explosive growth of online social networks. Prior studies on this problem have the following two limitations: i) they assume that there is only one pair of source and target at one time; and ii) the intermediate user can indefinitely forward friending requests. These two assumptions are impractical for online social networks. To bridge this gap, we study how to maximize the acceptance probability for multi-pair active friending in online social networks. In this paper, we first establish a probability model for the Multi-pair Active Friending with Tolerance and then propose two efficient algorithms: Multi-pair Active Friending algorithm for Conflict Resolution (MAFCR) and Group-based Multi-pair Active Friending algorithm for Conflict Resolution (MAFCR-G). Through extensive experiments, we show that the algorithm MAFCR significantly outperforms the baseline algorithm in terms of average acceptance probability, and the algorithm MAFCR-G greatly reduces the running time while obtaining similar average acceptance probability.

Recently, reconciling social networks receives significant attention. Most of the existing studies have limitations in the following three aspects: multiplicity, comprehensiveness and robustness. To address these three limitations, we rethink this problem and propose the MASTER framework, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation. In this framework, we first design a novel Constrained Dual Embedding model by simultaneously embedding and reconciling multiple social networks to formulate our problem into a unified optimization. To address this optimization, we then design an effective algorithm called NS-Alternating. We also prove that this algorithm converges to KKT points. Through extensive experiments on real-world datasets, we demonstrate that MASTER outperforms the state-of-the-art approaches.

This paper studies the problem of identifying influencers on specific topics in the microblog sphere. Prior works usually use the cumulative number of social links to measure users' topic-level influence, which ignores the dynamics of influence. As a result, they usually find faded influencers. To address the limitations of prior methods, we propose a novel probabilistic generative model to capture the variation of influence over time. Then a influence decay method is proposed to measure users' current topic-level influence.

In network virtualization, one of the key problems is to embed a sequence of virtual networks with both node and link constraints onto the physical network, which is known to be NP-hard. When a virtual network arrives, the recent studies focus on designing a solution to minimize the energy cost while maximizing the revenue of the physical network at that time. However, after some time, due to the significant dynamics of the resources of the physical network, the previous solution may become less energy efficient. In this paper, we take a further step and study how to re-optimize the energy cost by leveraging the migration technique. In particular, we first model the pros (e.g., energy saving) and cons (e.g., interruption time and bandwidth waste) of migration. Then we design a heuristic energy aware virtual network migration algorithm called EA-VNM. It answers the following key questions: when performing migration, migrate which virtual nodes to where, and how to perform migration. Extensive simulations show that our algorithm significantly reduces the energy cost by up to 25% over the state-of-the-art algorithm while maintaining similar revenue.

In network virtualization, how to efficiently embed virtual networks with both node and link demands into a shared physical network, namely virtual network embedding, has attracted significant attention. Most of prior studies on this problem have the following two limitations: i) they assumed that the virtual network demands are constant values, which does not hold in real-world network since such demands may vary a lot over time; ii) their primary goal was to generate more revenues for the physical network, with no consideration of the energy cost, which has become a critical issue for the physical network. In this paper, we bridge the gap and study the energy aware virtual network embedding with dynamic demands. Specifically, we first model the dynamics of virtual network demands as a combination of following Gaussian distribution and exhibiting daily diurnal pattern. We then design an efficient heuristic algorithm by leveraging the dynamic characteristic of virtual network demands to minimize the energy consumption while keeping high revenue for the physical network. We implemented our algorithm in C++ and performed side-by-side comparison with prior algorithm. Extensive simulations show that our algorithm can significantly reduce the energy cost by up to 16% over the state-of-the-art algorithm, while maintaining near the same revenue.

Virtual network embedding, which means mapping virtual networks requested by users to a shared substrate network maintained by an Internet service provider, is a key function that network virtualization needs to provide. Prior work on virtual network embedding has primarily focused on maximizing the revenue of the Internet service provider and did not consider the energy cost in accommodating such requests. As energy cost is more than half of the operating cost of the substrate networks, while trying to accommodate more virtual network requests, minimizing energy cost is critical for infrastructure providers. In this paper, we make the first effort toward energy-aware virtual network embedding. We first propose an energy cost model and formulate the energy-aware virtual network embedding problem as an integer linear programming problem. We then propose two efficient energy-aware virtual network embedding algorithms: a heuristic-based algorithm and a particle-swarm-optimization-technique-based algorithm. We implemented our algorithms in C++ and performed side-by-side comparison with prior algorithms. The simulation results show that our algorithms significantly reduce the energy cost by up to 50% over the existing algorithm for accommodating the same sequence of virtual network requests.

In light of rapid increase of electricity cost, many business organizations have to find new ways to cut the electricity bill. This paper studies how to reduce the electricity cost in geographical inter-domain virtual network embedding, which embeds virtual networks requested by users to multiple geographically distributed substrate networks run by an infrastructure provider. Previous researches have primarily focused on finding embedding methods to increase revenues by accommodating more virtual network requests, with little attention to reducing the electricity cost. To bridge this gap, we formulate an electricity cost model and design an efficient cost-aware virtual network embedding algorithm by exploiting the location-varying and time-varying diversities of the electricity price and optimizing the energy consumption. Through extensive simulations, we show that our algorithm can significantly reduce the electricity cost by up to 21% over the existing cost-oblivious algorithm, while maintaining nearly the same revenues for the infrastructure provider.

Bulk data transfers account for a large portion of inter-datacenter traffic, such as backups, propagation of bulky updates and migration of data. These bulk data transfers not only consume massive inter-datacenter bandwidth, but also increase the transmission cost of datacenters. To solve this problem, we first employ the max-min fairness to the design of optimal multiple bulk data transfers scheduling algorithm, which leverages the delay tolerance nature of bulk data and reuses dynamic leftover bandwidth to complete multiple bulk data transfers. Then we apply time-expanded technique to transform the problem under a dynamic network into a static network multi-flow problem, and solve it simultaneously from both routing assignment and bandwidth allocation through iterative linear programming approach. Extensive simulations are conducted on a real datacenter topology to demonstrate that our solutions can: 1) improve the network resource utilization; 2) minimize the average bulk data transfer completion time.

In recent years large graph processing has emerged to be a popular application for companies because of the increasing large Web graph and social networks. The ever growing scale of graphs and recent emergence of cloud computing poses challenges to their efficient and cost-conscious scheduling approach for processing tasks. In this paper, we focus on the use of cloud resources for dispatching large graph processing tasks. We design a novel framework EComer that can be easily integrated into existing cloud infrastructure. The key component of this framework is a cost-conscious scheduling heuristic, called CCSH, which is an extension of Heterogeneous Earliest Finish Time (HEFT). Our algorithm CCSH first constructs a priority list of tasks and then assigns the task with the highest priority value to the cost-efficient virtual machine in a cloud setting. The comparison study, based on randomly generated large graphs and a real-life astronomy application model, demonstrates that our algorithm outperforms HEFT by exhibiting significant monetary cost savings at a reasonable increase in overall execution time.