Exploring User-Provided Connectivity

Network services often exhibit positive and negative externalities that affect users\u27 adoption decisions. One such service is user-provided connectivity or UPC. The service offers an alternative to traditional infrastructure-based communication services by allowing users to share their home base connectivity with other users, thereby increasing their access to connectivity. More users mean more connectivity alternatives, i.e., a positive externality, but also greater odds of having to share one\u27s own connectivity, i.e., a negative externality. The tug of war between positive and negative externalities together with the fact that they often depend not just on how many but also which users adopt, make it difficult to predict the service\u27s eventual success. Exploring this issue is the focus of this paper, which investigates not only when and why such services may be viable, but also explores how pricing can be used to effectively and practically realize them

Exploring User-Provided Connectivity - A Simple Model

The advent of cheap and ubiquitous wireless access has introduced a number of new connectivity paradigms. This paper investigates one of them, user-provided connectivity or UPC. In contrast to traditional infrastructure-based connectivity, e.g., connectivity through the up-front build-out of expensive base-stations, UPC realizes connectivity organically as users join and expand its coverage. The low(er) deployment cost this affords is one of its main attractions. Conversely, the disadvantages of connectivity sharing and a high barrier-to-entry from low initial penetration create strong disincentives to its adoption. The paper’s contributions are in formulating and solving a simple model that captures key aspects of UPC adoption, and in articulating guidelines to make it successful. For analytical tractability, the model is arguably simplistic, but the robustness of its findings is demonstrated numerically across a wide range of more general (and more realistic) configuration

Explainable Reasoning over Knowledge Graphs for Recommendation

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.Comment: 8 pages, 5 figures, AAAI-201

Weak nodes detection in urban transport systems: Planning for resilience in Singapore

The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g. floods terroristic attacks, etc...). In this perspective, we propose ACHILLES, an application to model people's movements in a given transport system mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.Comment: 9 pages, 6 figures, IEEE Data Science and Advanced Analytic

Exploring Bluetooth based Mobile Phone Interaction with the Hermes Photo Display

One of the most promising possibilities for supporting user interaction with public displays is the use of personal mobile phones. Furthermore, by utilising Bluetooth users should have the capability to interact with displays without incurring personal financial connectivity costs. However, despite the relative maturity of Bluetooth as a standard and its widespread adoption in today’s mobile phones, little exploration seems to have taken place in this area - despite its apparent significant potential. This paper describe the findings of an exploratory study nvolving our Hermes Photo Display which has been extended to enable users with a suitable phone to both send and receive pictures over Bluetooth. We present both the technical challenges of working with Bluetooth and, through our user study, we present initial insights into general user acceptability issues and the potential for such a display to facilitate notions of community

Time as a limited resource: Communication Strategy in Mobile Phone Networks

We used a large database of 9 billion calls from 20 million mobile users to examine the relationships between aggregated time spent on the phone, personal network size, tie strength and the way in which users distributed their limited time across their network (disparity). Compared to those with smaller networks, those with large networks did not devote proportionally more time to communication and had on average weaker ties (as measured by time spent communicating). Further, there were not substantially different levels of disparity between individuals, in that mobile users tend to distribute their time very unevenly across their network, with a large proportion of calls going to a small number of individuals. Together, these results suggest that there are time constraints which limit tie strength in large personal networks, and that even high levels of mobile communication do not fundamentally alter the disparity of time allocation across networks.Comment: 10 pages, 3 figures. Accepted for publication in Social Network

Neural Graph Collaborative Filtering

Learning vector representations (aka. embeddings) of users and items lies at the core of modern recommender systems. Ranging from early matrix factorization to recently emerged deep learning based methods, existing efforts typically obtain a user's (or an item's) embedding by mapping from pre-existing features that describe the user (or the item), such as ID and attributes. We argue that an inherent drawback of such methods is that, the collaborative signal, which is latent in user-item interactions, is not encoded in the embedding process. As such, the resultant embeddings may not be sufficient to capture the collaborative filtering effect. In this work, we propose to integrate the user-item interactions -- more specifically the bipartite graph structure -- into the embedding process. We develop a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it. This leads to the expressive modeling of high-order connectivity in user-item graph, effectively injecting the collaborative signal into the embedding process in an explicit manner. We conduct extensive experiments on three public benchmarks, demonstrating significant improvements over several state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF. Codes are available at https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from the version published in ACM Digital Librar