1,009 research outputs found
Node aggregation for enhancing PageRank
In this paper, we study the problem of node aggregation under different perspectives for
increasing PageRank of some nodes of interest. PageRank is one of the parameters used by the search engine
Google to determine the relevance of a web page. We focus our attention to the problem of nding the best
nodes in the network from an aggregation viewpoint, i.e., what are the best nodes to merge with for the given
nodes. This problem is studied from global and local perspectives. Approximations are proposed to reduce
the computation burden and to overcome the limitations resulting from the lack of centralized information.
Several examples are presented to illustrate the different approaches that we proposeFP7-ICT Project DYMASOS under Grant 611281Ministerio de EconomÃa y Competitividad COOPERA Project Grant DPI2013-46912-C2-1-RMinisterio de Educación José Castillejo Grant (CAS14/00277)Grant-in-Aid for Scientific Research Grant 15H04020 and Fellowship PE1604
A multi-class approach for ranking graph nodes: models and experiments with incomplete data
After the phenomenal success of the PageRank algorithm, many researchers have
extended the PageRank approach to ranking graphs with richer structures beside
the simple linkage structure. In some scenarios we have to deal with
multi-parameters data where each node has additional features and there are
relationships between such features.
This paper stems from the need of a systematic approach when dealing with
multi-parameter data. We propose models and ranking algorithms which can be
used with little adjustments for a large variety of networks (bibliographic
data, patent data, twitter and social data, healthcare data). In this paper we
focus on several aspects which have not been addressed in the literature: (1)
we propose different models for ranking multi-parameters data and a class of
numerical algorithms for efficiently computing the ranking score of such
models, (2) by analyzing the stability and convergence properties of the
numerical schemes we tune a fast and stable technique for the ranking problem,
(3) we consider the issue of the robustness of our models when data are
incomplete. The comparison of the rank on the incomplete data with the rank on
the full structure shows that our models compute consistent rankings whose
correlation is up to 60% when just 10% of the links of the attributes are
maintained suggesting the suitability of our model also when the data are
incomplete
Dynamic re-optimization techniques for stream processing engines and object stores
Large scale data storage and processing systems are strongly motivated by the need to store and analyze massive datasets. The complexity of a large class of these systems is rooted in their distributed nature, extreme scale, need for real-time response, and streaming nature. The use of these systems on multi-tenant, cloud environments with potential resource interference necessitates fine-grained monitoring and control. In this dissertation, we present efficient, dynamic techniques for re-optimizing stream-processing systems and transactional object-storage systems.^ In the context of stream-processing systems, we present VAYU, a per-topology controller. VAYU uses novel methods and protocols for dynamic, network-aware tuple-routing in the dataflow. We show that the feedback-driven controller in VAYU helps achieve high pipeline throughput over long execution periods, as it dynamically detects and diagnoses any pipeline-bottlenecks. We present novel heuristics to optimize overlays for group communication operations in the streaming model.^ In the context of object-storage systems, we present M-Lock, a novel lock-localization service for distributed transaction protocols on scale-out object stores to increase transaction throughput. Lock localization refers to dynamic migration and partitioning of locks across nodes in the scale-out store to reduce cross-partition acquisition of locks. The service leverages the observed object-access patterns to achieve lock-clustering and deliver high performance. We also present TransMR, a framework that uses distributed, transactional object stores to orchestrate and execute asynchronous components in amorphous data-parallel applications on scale-out architectures
Label-free Node Classification on Graphs with Large Language Models (LLMS)
In recent years, there have been remarkable advancements in node
classification achieved by Graph Neural Networks (GNNs). However, they
necessitate abundant high-quality labels to ensure promising performance. In
contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency
on text-attributed graphs. Yet, they face challenges in efficiently processing
structural data and suffer from high inference costs. In light of these
observations, this work introduces a label-free node classification on graphs
with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs
while mitigating their limitations. Specifically, LLMs are leveraged to
annotate a small portion of nodes and then GNNs are trained on LLMs'
annotations to make predictions for the remaining large portion of nodes. The
implementation of LLM-GNN faces a unique challenge: how can we actively select
nodes for LLMs to annotate and consequently enhance the GNN training? How can
we leverage LLMs to obtain annotations of high quality, representativeness, and
diversity, thereby enhancing GNN performance with less cost? To tackle this
challenge, we develop an annotation quality heuristic and leverage the
confidence scores derived from LLMs to advanced node selection. Comprehensive
experimental results validate the effectiveness of LLM-GNN. In particular,
LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with
a cost less than 1 dollar.Comment: The code will be available soon via
https://github.com/CurryTang/LLMGN
Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks
The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks
Toward Entity-Aware Search
As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability
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