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- All Subjects: Negative Queries
- All Subjects: Personalized PageRank
- Creators: Sapino, Maria Luisa
Description
Node proximity measures are commonly used for quantifying how nearby or otherwise related to two or more nodes in a graph are. Node significance measures are mainly used to find how much nodes are important in a graph. The measures of node proximity/significance have been highly effective in many predictions and applications. Despite their effectiveness, however, there are various shortcomings. One such shortcoming is a scalability problem due to their high computation costs on large size graphs and another problem on the measures is low accuracy when the significance of node and its degree in the graph are not related. The other problem is that their effectiveness is less when information for a graph is uncertain. For an uncertain graph, they require exponential computation costs to calculate ranking scores with considering all possible worlds.
In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values.
In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values.
ContributorsKim, Jung Hyun (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Tong, Hanghang (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2017
Description
Similarity search in high-dimensional spaces is popular for applications like image
processing, time series, and genome data. In higher dimensions, the phenomenon of
curse of dimensionality kills the effectiveness of most of the index structures, giving
way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity
searches. In addition to range searches and k-nearest neighbor searches, there
is a need to answer negative queries formed by excluded regions, in high-dimensional
data. Though there have been a slew of variants of LSH to improve efficiency, reduce
storage, and provide better accuracies, none of the techniques are capable of
answering queries in the presence of excluded regions.
This thesis provides a novel approach to handle such negative queries. This is
achieved by creating a prefix based hierarchical index structure. First, the higher
dimensional space is projected to a lower dimension space. Then, a one-dimensional
ordering is developed, while retaining the hierarchical traits. The algorithm intelligently
prunes the irrelevant candidates while answering queries in the presence of
excluded regions. While naive LSH would need to filter out the negative query results
from the main results, the new algorithm minimizes the need to fetch the redundant
results in the first place. Experiment results show that this reduces post-processing
cost thereby reducing the query processing time.
processing, time series, and genome data. In higher dimensions, the phenomenon of
curse of dimensionality kills the effectiveness of most of the index structures, giving
way to approximate methods like Locality Sensitive Hashing (LSH), to answer similarity
searches. In addition to range searches and k-nearest neighbor searches, there
is a need to answer negative queries formed by excluded regions, in high-dimensional
data. Though there have been a slew of variants of LSH to improve efficiency, reduce
storage, and provide better accuracies, none of the techniques are capable of
answering queries in the presence of excluded regions.
This thesis provides a novel approach to handle such negative queries. This is
achieved by creating a prefix based hierarchical index structure. First, the higher
dimensional space is projected to a lower dimension space. Then, a one-dimensional
ordering is developed, while retaining the hierarchical traits. The algorithm intelligently
prunes the irrelevant candidates while answering queries in the presence of
excluded regions. While naive LSH would need to filter out the negative query results
from the main results, the new algorithm minimizes the need to fetch the redundant
results in the first place. Experiment results show that this reduces post-processing
cost thereby reducing the query processing time.
ContributorsBhat, Aneesha (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Sarwat, Mohamed (Committee member) / Arizona State University (Publisher)
Created2016