The diagnosis of autism spectrum disorder (ASD) is often hampered by its heterogeneity and reliance on time-consuming behavioral assessments. Automated neuroimaging-based diagnostic tools offer a promising alternative, but multi-site data integration often introduces variability, hindering the achievement of accurate and interpretable results. This study presents the Connectome Convolutional Transformer (CCTF), a multimodal deep learning framework that integrates functional and structural brain connectivity information from fMRI and sMRI modalities. The CCTF enriches feature representation by incorporating diverse functional connectivity metrics and structural covariance networks based on multiple morphological properties. It employs a connectome convolutional embedding module and transformer encoder to capture and refine brain connectivity patterns. In addition, a node-to-graph pooling layer facilitates the identification of potential ASD biomarkers. Evaluation on the multi-site ABIDE dataset demonstrated that CCTF outperformed state-of-the-art methods, achieving accuracies of [Formula: see text] for fMRI, [Formula: see text] for sMRI, and [Formula: see text] for the ensemble fMRI+sMRI model in intra-site cross-validation. In the inter-site leave-one-site-out cross-validation, the CCTF maintained its superiority, with the ensemble model reaching [Formula: see text] accuracy, underscoring its robustness and generalizability across different sites. The identified brain regions are consistent with established ASD neurobiology, underscoring CCTF's potential to advance the understanding of the neural mechanisms underlying this complex disorder.

Complex networks have now become integral parts of modern information infrastructures. This paper proposes a user-centric method for detecting anomalies in heterogeneous information networks, in which nodes and/or edges might be from different types. In the proposed anomaly detection method, users interact directly with the system and anomalous entities can be detected through queries. Our approach is based on tensor decomposition and clustering methods. We also propose a network generation model to construct synthetic heterogeneous information network to test the performance of the proposed method. The proposed anomaly detection method is compared with state-of-the-art methods in both synthetic and real-world networks. Experimental results show that the proposed tensor-based method considerably outperforms the existing anomaly detection methods.

Nowadays, a big part of people rely on available content in social media in their decisions (e.g., reviews and feedback on a topic or product). The possibility that anybody can leave a review provides a golden opportunity for spammers to write spam reviews about products and services for different interests. Identifying these spammers and the spam content is a hot topic of research, and although a considerable number of studies have been done recently toward this end, but so far the methodologies put forth still barely detect spam reviews, and none of them show the importance of each extracted feature type. In this paper, we propose a novel framework, namedNetSpam, which utilizes spam features for modeling review data sets as heterogeneous information networks to map spam detection procedure into a classification problem in such networks. Using the importance of spam features helps us to obtain better results in terms of different metrics experimented on real-world review data sets from Yelp and Amazon Web sites. The results show thatNetSpamoutperforms the existing methods and among four categories of features, including review-behavioral, user-behavioral, review-linguistic, and user-linguistic, the first type of features performs better than the other categories.

How does one develop a new online community that is highly engaging to each user and promotes social interaction? A number of websites offer friend-finding features that help users bootstrap social networks on the website by copying links from an established network like Facebook or Twitter. This paper quantifies the extent to which such social bootstrapping is effective in enhancing a social experience of the website. First, we develop a stylised analytical model that suggests that copying tends to produce a giant connected component (i.e., a connected community) quickly and preserves properties such as reciprocity and clustering, up to a linear multiplicative factor. Second, we use data from two websites, Pinterest and Last.fm, to empirically compare the subgraph of links copied from Facebook to links created natively. We find that the copied subgraph has a giant component, higher reciprocity and clustering, and confirm that the copied connections see higher social interactions. However, the need for copying diminishes as users become more active and influential. Such users tend to create links natively on the website, to users who are more similar to them than their Facebook friends. Our findings give new insights into understanding how bootstrapping from established social networks can help engage new users by enhancing social interactivity.

The current multi-channel P2P video streaming architectures still suffer from several performance problems such as low Quality of Service (QoS) in unpopular channels. The P2P systems are inherently dynamic, and their performance problems could be categorized into four groups, peer churn, channel churn, uncooperative peers, and geographical distribution of peers. In this paper, for the first time, we develop a novel locality-incentive framework for multi-channel live video streaming. We propose a hierarchical overlay network architecture by utilizing a dual-mode locality-awareness method (spatial and temporal). Moreover, an incentive mechanism for encouraging peers to dedicate their upload bandwidth is introduced. Finally, an efficient buffer-map structure is proposed to predict the validation time of each video chunk request as well as the receiving time of video chunks. We have evaluated the performance of our framework via extensive simulations and compared it with the state-of-the-art method in multi-channel systems. The simulation results demonstrate that the quality of unpopular channels is improved by up to 38%. Moreover, the proposed method improved the quality of video by reducing the playback delay (up to 27%), distortion (up to 39%), and reducing the redundant traffic into the Internet backbones (up to 43%).

This paper presents a new Compressive Sensing (CS) scheme for detecting network congested links. We focus on decreasing the required number of measurements to detect all congested links in the context of network tomography. We have expanded the LASSO objective function by adding a new term corresponding to the prior knowledge based on the relationship between the congested links and the corresponding link Betweenness Centrality (BC). The accuracy of the proposed model is verified by simulations on two real datasets. The results demonstrate that our model outperformed the state-of-the-art CS based method with significant improvements in terms of F-Score.

Partially-observed network data collected by link-tracing based sampling methods is often being studied to obtain the characteristics of a large complex network. However, little attention has been paid to sampling from directed networks such as WWW and Peer-to-Peer networks. In this paper, we propose a novel two-step (sampling/estimation) framework to measure nodal characteristics which can be defined by an average target function in an arbitrary directed network. To this end, we propose a personalized PageRank-based algorithm to visit and sample nodes. This algorithm only uses already visited nodes as local information without any prior knowledge about the latent structure of the network. Moreover, we introduce a new estimator based on the approximate importance sampling to estimate average target functions. The proposed estimator utilizes calculated PageRank value of each sampled node as an approximation for the exact visiting probability. To the best of our knowledge, this is the first study on correcting the bias of a sampling method by re-weighting of measured values that considers the effect of approximation of visiting probabilities. Comprehensive theoretical and empirical analysis of the estimator demonstrate that it is asymptotically unbiased even in situations where stationary distribution of PageRank is poorly approximated.

The quality of service inWireless Multimedia Sensor Networks (WMSN) is related to packet loss rate. Recently different studies have been done on developing efficient protocols in the transport layer for controlling packet loss in WMSN. However, all of these protocols are independent of the characteristics of multimedia content. In this paper, a novel transport layer protocol, called User Datagram Dispatcher Protocol (UDDP), is proposed to minimize the packet loss ratio in WMSN by considering traffic characteristics, the inter-arrival pattern of packets and packet priority. UDDP is a new cross-layer transport layer that uses information of MAC and application layers to distribute packet arrivals in a GOP. The experimental results show that our protocol provides performance improvement in terms of high video quality, packet loss, energy conservation and delay compared to other protocols such as CCF and PCCP.

Using Peer-to-Peer (P2P) overlay networks have become a progressively popular approach for streaming live media over the Internet due to their deployment simplicity and scalability. In this paper, we propose a new hybrid push-pull live P2P video streaming protocol called PPM that combines the benefits of pull and push mechanisms for video delivery. Our main goal is to minimize the network end-to-end delay compared to the pure mesh networks. The PPM consists of two phases; Pull-based and Push-based. In the first phase, a new peer joins to the network based on a pull-based mechanism. In the second phase, a parent node based on the peers' overlay hop count in the mesh topology is selected. Then, a dynamic tree is constructed to push the high priority video frames to the children of the selected parent. Using OMNET++ as the simulation platform, we show that beside significant improvement on the end-to-end delay, PPM achieves lower visual distortion compared to the pure mesh networks. Moreover, the simulation results confirm superiority of the PPM in comparison with the popular mesh-based P2P streaming systems.

Twitter as one of the most important microblogging online social networks has attracted more than 200 million users in recent years. Although there have been several attempts on characterizing the Twitter by using incomplete sampled data, they have not been very successful to estimate the characteristics of the whole network. In this paper, we characterize Twitter by sampling from its social graph and user behaviors through a random walk based sampling technique called Respondent-Driven Sampling (RDS). To the best of our knowledge, for the first time RDS method and its estimator are used in order to obtain uniform unbiased estimation of several key structural and behavioral properties of Twitter. We compared the performance of the proposed method with other sampling methods such as Metropolis-Hasting Random Walk (MHRW) and sampling from active users (Timeline) against the uniform sampling (UNI). In order to gather the required data, we have implemented four independent crawlers. Our experimental results indicate that the RDS method exhibits lower estimation errors to the sample in- and out-degree distribution compared to MHRW and Timeline. We also show that RDS is more suitable to sample the followers vs. followings ratio, and the correlation between followers/followings vs. tweets.