46 research outputs found
Peering Strategic Game Models for Interdependent ISPs in Content Centric Internet
Emergent content-oriented networks prompt Internet service providers (ISPs) to evolve and take major responsibility for content delivery. Numerous content items and varying content popularities motivate interdependence between peering ISPs to elaborate their content caching and sharing strategies. In this paper, we propose the concept of peering for content exchange between interdependent ISPs in content centric Internet to minimize content delivery cost by a proper peering strategy. We model four peering strategic games to formulate four types of peering relationships between ISPs who are characterized by varying degrees of cooperative willingness from egoism to altruism and interconnected as profit-individuals or profit-coalition. Simulation results show the price of anarchy (PoA) and communication cost in the four games to validate that ISPs should decide their peering strategies by balancing intradomain content demand and interdomain peering relations for an optimal cost of content delivery
Information Exchange rather than Topology Awareness: Cooperation between P2P Overlay and Traffic Engineering
Solutions to the routing strategic conflict between noncooperative P2P overlay and ISP underlay go separate ways: hyperselfishness and cooperation. Unpredictable (possibly adverse) impact of the hyperselfish topology awareness, which is adopted in both overlay routing and traffic engineering, has not been sufficiently studied in the literature. Topology-related information exchange in a cooperatively efficient way should be highlighted to alleviate the cross-layer conflict. In this paper, we first illustrate the hyperselfish weakness with two dynamic noncooperative game models in which hyperselfish overlay or underlay has to accept a suboptimal profit. Then we build a synergistic cost-saving (SC) game model to reduce the negative effects of noncooperation. In the SC model, through information exchange, that is, the classified path-delay metrics for P2P overlay and peer locations for underlay, P2P overlay selects proximity as well as saving traffic transit cost for underlay, and ISP underlay adjusts routing to optimize network cost as well as indicating short delay paths for P2P. Simulations based on the real and generated topologies validate cost improvement by SC model and find a proper remote threshold value to limit P2P traffic from remote area, cross-AS, or cross-ISP
Estimating Early Fundraising Performance of Innovations via Graph-based Market Environment Model
Well begun is half done. In the crowdfunding market, the early fundraising
performance of the project is a concerned issue for both creators and
platforms. However, estimating the early fundraising performance before the
project published is very challenging and still under-explored. To that end, in
this paper, we present a focused study on this important problem in a market
modeling view. Specifically, we propose a Graph-based Market Environment model
(GME) for estimating the early fundraising performance of the target project by
exploiting the market environment. In addition, we discriminatively model the
market competition and market evolution by designing two graph-based neural
network architectures and incorporating them into the joint optimization stage.
Finally, we conduct extensive experiments on the real-world crowdfunding data
collected from Indiegogo.com. The experimental results clearly demonstrate the
effectiveness of our proposed model for modeling and estimating the early
fundraising performance of the target project
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and
crucial task in graph data analysis. This task aims to predict nodes from
unseen classes which are unobserved in the training process. Existing work
mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes
and labels' semantics thus enabling knowledge transfer from seen to unseen
classes. However, the multi-faceted semantic orientation in the
feature-semantic alignment has been neglected by previous work, i.e. the
content of a node usually covers diverse topics that are relevant to the
semantics of multiple labels. It's necessary to separate and judge the semantic
factors that tremendously affect the cognitive ability to improve the
generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted
framework (KMF) that enhances the richness of label semantics via the extracted
KG (Knowledge Graph)-based topics. And then the content of each node is
reconstructed to a topic-level representation that offers multi-faceted and
fine-grained semantic relevancy to different labels. Due to the particularity
of the graph's instance (i.e., node) representation, a novel geometric
constraint is developed to alleviate the problem of prototype drift caused by
node information aggregation. Finally, we conduct extensive experiments on
several public graph datasets and design an application of zero-shot
cross-domain recommendation. The quantitative results demonstrate both the
effectiveness and generalization of KMF with the comparison of state-of-the-art
baselines
Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors
In the modern e-commerce, the behaviors of customers contain rich
information, e.g., consumption habits, the dynamics of preferences. Recently,
session-based recommendations are becoming popular to explore the temporal
characteristics of customers' interactive behaviors. However, existing works
mainly exploit the short-term behaviors without fully taking the customers'
long-term stable preferences and evolutions into account. In this paper, we
propose a novel Behavior-Intensive Neural Network (BINN) for next-item
recommendation by incorporating both users' historical stable preferences and
present consumption motivations. Specifically, BINN contains two main
components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning.
Firstly, a novel item embedding method based on user interactions is developed
for obtaining an unified representation for each item. Then, with the embedded
items and the interactive behaviors over item sequences, BINN discriminatively
learns the historical preferences and present motivations of the target users.
Thus, BINN could better perform recommendations of the next items for the
target users. Finally, for evaluating the performances of BINN, we conduct
extensive experiments on two real-world datasets, i.e., Tianchi and JD. The
experimental results clearly demonstrate the effectiveness of BINN compared
with several state-of-the-art methods.Comment: 10 pages, 7 figures, KDD 201