On matrix factorization and finite-time average-consensus

We study the finite-time average-consensus problem for arbitrary connected networks. Viewing this consensus problem as a factorization of 1/n11^T by suitable families of matrices, we prove the existence of a finite factorization and provide tight bounds on the size of the minimal factorization by exhibiting finite-time average-consensus algorithms and bounding their runtimes. We also show that basic matrix theory yields insights into the structure of finite-time consensus algorithms

Herding Effect based Attention for Personalized Time-Sync Video Recommendation

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201

Spectral properties of transfer operators

Ph.DDOCTOR OF PHILOSOPH

Fair and efficient router congestion control

Congestion is a natural phenomenon in any network queuing system, and is unavoidable if the queuing system is operated near capacity. In this paper we study how to set the rules of a queuing system so that all the users have a self-interest in controlling congestion when it happens. Routers in the internet respond to local congestion by dropping packets. But if packets are dropped indiscriminately, the effect can be to encourage senders to actually increase their transmission rates, worsening the congestion and destabilizing the system. Alternatively, and only slightly more preferably, the effect can be to arbitrarily let a few insistent senders take over most of the router capacity. We approach this problem from first principles: a router packet-dropping protocol is a mechanism that sets up a game between the senders, who are in turn competing for link capacity. Our task is to design this mechanism so that the game equilibrium is desirable: high total rate is achieved and is shared widely among all senders. In addition, equilibrium should be reestablished quickly in response to changes in transmission rates. Our solution is based upon auction theory: in principle, although not always in practice, we drop packets of the highest-rate sender, in case of congestion. We will prove the game-theoretic merits of our method. We'll also describe a variant of the method with some further advantages that will be supported by network simulations

Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i.e., the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.Comment: Accepted by AAAI 202

Fair and efficient router congestion control

Congestion is a natural phenomenon in any network queuing system, and is unavoidable if the queuing system is operated near capacity. In this paper we study how to set the rules of a queuing system so that all the users have a self-interest in controlling congestion when it happens. Routers in the internet respond to local congestion by dropping packets. But if packets are dropped indiscriminately, the effect can be to encourage senders to actually increase their transmission rates, worsening the congestion and destabilizing the system. Alternatively, and only slightly more preferably, the effect can be to arbitrarily let a few insistent senders take over most of the router capacity. We approach this problem from first principles: a router packet-dropping protocol is a mechanism that sets up a game between the senders, who are in turn competing for link capacity. Our task is to design this mechanism so that the game equilibrium is desirable: high total rate is achieved and is shared widely among all senders. In addition, equilibrium should be reestablished quickly in response to changes in transmission rates. Our solution is based upon auction theory: in principle, although not always in practice, we drop packets of the highest-rate sender, in case of congestion. We will prove the game-theoretic merits of our method. We'll also describe a variant of the method with some further advantages that will be supported by network simulations