![the query, then all the objects in the target
cluster are likely not in the final top-k lists and
can be pruned. By partitioning in different
feature clusters, cheaper calculations on the
dimension-reduced query vector and candidate
vectors can be used to derive the similarity
upper bounds. The PathSim-Pruning can
significantly improve the query processing speed
comparing with the baseline algorithm, without
affecting the search quality.
c) Multiple Meta-Paths Combination
In the previous section, we presented algorithms
for similarity search using single meta-path.
Now, we present a solution to combine multiple
meta-paths. The reason why we need to combine
several meta-paths is that each meta-path
provides a unique angle to view the similarity
between objects, and the ground truth may be a
cause of different factors. Some useful guidance
of the weight assignment includes: longer meta-
path utilize more remote relationship and thus
should be assigned with a smaller weight, such
as in P-PageRank and SimRank and meta-paths
with more important relationships should be
assigned with a higher weight. For automatically
determining the weights, users cloud provides
training examples of similar objects to learn the
weights of different meta-paths using learning
algorithm.
V. EXPECTED RESULTS
To show the effectiveness of the PathSim
measure and the efficiency of the proposed
algorithms we use the bibliographic networks
extracted from DBLP and Flicker in the
experiments.
The PathSim algorithm significantly improves
the query processing speed comparing with the
baseline algorithm, without affecting the search
quality.
For additional case studies, we construct a
Flicker network from a subset of the Flicker data
which contains four types of objects such as
images, users, tags, and groups. We have to
show that our algorithms improve similarity
search between object based on the potentiality
and correlation between objects.
VI. CONCLUSION
In this paper we introduced novel similarity
search using meta-path based similarity search
using baseline algorithm and co-clustering based
pruning algorithms to improve the similarity
search based on the strengths and relationships
between objects.
REFERENCES
[1] Jiawei Han, Lise Getoor, Wei Wang,
Johannes Gehrke, Robert Grossman "Mining
Heterogeneous Information Networks
Principles and Methodologies"
[2] Y. Koren, S.C. North, and C. Volinsky,
“Measuring and Extracting Proximity in
Networks,” Proc. 12th ACM SIGKDD Int’l
Conf. Knowledge Discovery and Data
Mining, pp. 245-255, 2006.
[3] M. Ito, K. Nakayama, T. Hara, and S. Nishio,
“Association Thesaurus Construction
Methods Based on Link Co-Occurrence
321
INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT
ISBN: 378 - 26 - 138420 - 5
www.iaetsd.in](https://image.slidesharecdn.com/iaetsd-similaritysearchininformationnetworksusing-150407215815-conversion-gate01/85/Iaetsd-similarity-search-in-information-networks-using-5-320.jpg)
![Analysis for Wikipedia,” Proc. 17th ACM
Conf. Information and Knowledge
Management (CIKM), pp. 817-826, 2008.
[4] K. Nakayama, T. Hara, and S. Nishio,
“Wikipedia Mining for an Association Web
Thesaurus Construction,” Proc. Eighth Int’l
Conf. Web Information Systems Eng.
(WISE), pp. 322-334, 2007.
[5] M. Yazdani and A. Popescu-Belis, “A
Random Walk Framework to Compute
Textual Semantic Similarity: A Unified
Model for Three Benchmark Tasks,” Proc.
IEEE Fourth Int’l Conf. Semantic
Computing (ICSC), pp. 424-429, 2010.
[6] R.L. Cilibrasi and P.M.B. Vita´nyi, “The
Google Similarity Distance,” IEEE Trans.
Knowledge and Data Eng., vol. 19, no. 3,
pp. 370-383, Mar. 2007.
[7] G. Kasneci, F.M. Suchanek, G. Ifrim, M.
Ramanath, and G. Weikum, “Naga:
Searching and Ranking Knowledge,” Proc.
IEEE 24th Int’l Conf. Data Eng. (ICDE), pp.
953-962, 2008.
[8] R.K. Ahuja, T.L. Magnanti, and J.B. Orlin,
Network Flows: Theory, Algorithms, and
Applications. Prentice Hall, 1993.
322
INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT
ISBN: 378 - 26 - 138420 - 5
www.iaetsd.in](https://image.slidesharecdn.com/iaetsd-similaritysearchininformationnetworksusing-150407215815-conversion-gate01/85/Iaetsd-similarity-search-in-information-networks-using-6-320.jpg)
Iaetsd similarity search in information networks using
- 1. Similarity Search in Information Networks using Meta-Path Based between Objects Abstract – Real world physical and abstract data objects are interconnected, forming enormous, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Therefore, the quality analysis of large heterogeneous information networks poses new challenges. In current system, a generalized flow based method is introduced for measuring the relationship on Wikipedia by reflecting all three concepts: distance, connectivity and co-citation. By using the current approach we measure only relationships between objects rather than similarities. To address these problems we introduce a novel solution meta-path based similarity searching approach for dealing with heterogeneous information networks using a meta-path-based method. Under this framework, similarity search and other mining tasks of the network structure. Index terms – similarity search, information network, and meta-path based, clustering. I. INTRODUCTION Similarity search, which aims at locating the most relevant information for a query in large collections of datasets, has been widely studied in many applications. For example, in spatial database, people are interested in finding the k nearest neighbors for a given spatial object. Object similarity is also one of the most primitive concepts for object clustering and many other data mining functions. In a similar context, it is critical to provide effective similarity search functions in information networks, to find similar entities for a given entity. In a network of tagged images such as flicker, a user may be interested in search for the most similar pictures for a given picture. In an e-commerce system, a user would be interest in search for the most similar products for a given product. Different attribute- based similarity search, links play an important role for similarity search in information networks, especially when the full information about attributes for objects is difficult to obtain. There are a few studies leveraging link information in networks for similarity search, but most of these revisions are focused on homogeneous or bipartite networks such as P- PageRank and SimRank. These similarity measures disregard the subtlety of different types among objects and links. Adoption of such measures to heterogeneous networks his significant drawbacks: even if we just want to compare objects of the same type, going through link paths of different types leads to rather 317 INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT ISBN: 378 - 26 - 138420 - 5 www.iaetsd.in
- 2. different semantics meanings, and it makes little sense to mix them up and measure the similarity without distinguishing their semantics. To systematically distinguish the semantics among paths connecting two objects, we introduce a meta-path based similarity framework for objects of the same type in a heterogeneous network. A meta-path is a sequence of relations between object types, which defines a new composite relation between its starting type and ending type. The meta-path framework provides a powerful mechanism for a user to select appropriate similarity semantics, by choosing a proper meta-path, or learn it from a set of training examples of similar objects. The meta-path based similarity framework, and relate it to two well-known existing link-based similarity functions for homogeneous information networks. We define a novel similarity measure, PathSim that is able to find peer objects that are not only strongly connected with each other but also share similar visibility in the network. Moreover, we propose an efficient algorithm to support online top-k queries for such similarity search. II. A META-PATH BASED SIMILARITY MEASURE The similarity between two objects in a link- based similarity function is determined by how the objects are connected in a network, which can be described using paths. In a heterogeneous information network, due to the heterogeneity of the types of links, the way to connect two objects can be much more diverse. The schema connections represent different relationships between authors, each having some different semantic meaning. Now the questions are, given an arbitrary heterogeneous information network, is there any way systematically identify all the possible connection type between two objects types? In order to do so, we propose two important concepts in the following. a) Network Schema And Meta-Path First, given a complex heterogeneous information network, it is necessary to provide its Meta level description for better understanding the network. Therefore, we propose the concept of network scheme to describe the Meta structure of a network. The concept of network scheme is similar to that of the Entity – Relationship model in database systems, but only captures the entity type and their binary relations, without considering the attributes for each Entity type. Network schema serves as a template for a network, and tells how many types of objects there are in the network and where the possible links exist. b) Bibliographic Scheme and Meta-Path For the bibliographic network scheme, where an explicitly shows the direction of a relation. III. META-PATH BASED SIMILARITY FRAMEWORK Given a user-specified meta-path, several similarity measures can be defined for a pair of objects, and according to the path instances 318 INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT ISBN: 378 - 26 - 138420 - 5 www.iaetsd.in
- 3. between them following the met-path. There are several straightforward measures in the following. Path count: the number of path instances between objects. Random Walk: s(x, y) is the probability of the random walk that starts from x and ends with y following meta-path P, which is the sum of the probabilities of all the path instances. Pair wise random walk: for a meta-path P that can be decomposed into two shorter meta-paths with the same length is then the pair wise random walk probability starting from objects x and y and reaching the same middle object. In general, we can define a meta-path based similarity framework for two objects x and y. Note that P-PageRank and SimRank, two well- known network similarity functions, are weighted combinations of random walk measure or pair wise random walk measure, respectively, over meta-paths with different lengths in homogeneous networks. In order to use P- PageRank and SimRank in heterogeneous information networks. a) A Novel Similarity Measure There have been several similarity measures are presented and they are partially to either highly visible objects or highly concentrated objects but cannot capture the semantics of peer similarity. However, in many scenarios, finding similar objects in networks is to find similar peers, such as finding similar authors based on their fields and reputation, finding similar actors based on their movie styles and productivity and finding similar product. This motivated us to propose a new, meta-path based similarity measure, call PathSim that captures the subtle of peer similarity. The insight behind it is that two similar peer objects should not only be strongly connected, but also share comparable observations. As the relation of peer should be symmetric, we confine PathSim to symmetric meta-paths. The calculation of PathSim between any two objects of the same type given a certain meta-path involves matrix multiplication. In this paper, we only consider the meta-path in the round trip from, to guarantee its symmetry and therefore the symmetry of the PathSim measure. Properties of PathSim 1. Symmetric. 2. Self-maximum 3. Balance of visibility Although using meta-path based similarity we can define similarity between two objects given any round trip meta-paths. As primary eigenvectors can be used as authority ranking of objects, the similarity between two objects under an infinite meta-path can be viewed as a measure defined on their rankings. Two objects with more similar rankings scores will have higher similarity. In the next section we discuss online query processing for ingle meta-path. 319 INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT ISBN: 378 - 26 - 138420 - 5 www.iaetsd.in
- 4. IV. QUERY PROCESSING FOR SINGLE META- PATH Compared with P-PageRank and SimRank, the calculation is much more efficient, as it is a local graph measure. But still involves expensive matrix multiplication operations for top –k search functions, as we need to calculate the similarity between a query and every object of the same type in the network. One possible solution is to materialize all the meta-paths. In order to support fast online query processing for large-scale networks, we propose a methodology that partially materializes short length meta-paths and then concatenates them online to derive longer meta-path-based similarity. First, a baseline method is proposed, which computes the similarity between query object x and all the candidate object y of the same type. Next, a co-clustering based pruning method is proposed, which prunes candidate objects that are not promising according to their similarity upper bounds. Both algorithms return exact top-k results the given query. a) Baseline Suppose we know that the relation matrix for meta-path and the diagonal vector in order to get top-k objects with the highest similarity for the query, we need to compute the probability of objects. The straightforward baseline is: (1) first apply vector matrix multiplication (2) calculate probability of objects (3) sort the probability of objects and return top-k list in the final step. When a large matrix, the vector matrix computation will be too time consuming to check every possible object. This will be much more efficient than Pairwise computation between the query and all the objects of that type. We call baseline concatenation algorithm as PathSim-baseline. The PathSim-baseline algorithm is still time consuming if the candidate set is large. The time complexity of computing PathSim for each candidate, where is O(d) on average and O(m) in the worst case. We now propose a co-clustering based top-k concatenation algorithm, by which non-promising target objects are dynamically filtered out to reduce the search space. b) Co-Clustering-Based Pruning In the baseline algorithm, the computational costs involve two factors. First, the more candidates to check, the more time the algorithm will take; second, for each candidate, the dot product of query vector and candidate vector will at most involve m operations, where m is the vector length. Based on the intuition, we propose, we propose a co-clustering-based path concatenation method, which first generates co- clusters of two types of objects for partial relation matrix, then stores necessary statics for each of the blocks corresponding to different co- cluster pairs, and then uses the block statistics to prune the search space. For better picture, we call cluster of type as target clusters, since the objects are the targets for the query and call clusters of type as feature clusters. Since the objects serve as features to calculate the similarity between the query and the target objects. By partitioning into different target clusters, if a whole target cluster is not similar to 320 INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT ISBN: 378 - 26 - 138420 - 5 www.iaetsd.in
- 5. the query, then all the objects in the target cluster are likely not in the final top-k lists and can be pruned. By partitioning in different feature clusters, cheaper calculations on the dimension-reduced query vector and candidate vectors can be used to derive the similarity upper bounds. The PathSim-Pruning can significantly improve the query processing speed comparing with the baseline algorithm, without affecting the search quality. c) Multiple Meta-Paths Combination In the previous section, we presented algorithms for similarity search using single meta-path. Now, we present a solution to combine multiple meta-paths. The reason why we need to combine several meta-paths is that each meta-path provides a unique angle to view the similarity between objects, and the ground truth may be a cause of different factors. Some useful guidance of the weight assignment includes: longer meta- path utilize more remote relationship and thus should be assigned with a smaller weight, such as in P-PageRank and SimRank and meta-paths with more important relationships should be assigned with a higher weight. For automatically determining the weights, users cloud provides training examples of similar objects to learn the weights of different meta-paths using learning algorithm. V. EXPECTED RESULTS To show the effectiveness of the PathSim measure and the efficiency of the proposed algorithms we use the bibliographic networks extracted from DBLP and Flicker in the experiments. The PathSim algorithm significantly improves the query processing speed comparing with the baseline algorithm, without affecting the search quality. For additional case studies, we construct a Flicker network from a subset of the Flicker data which contains four types of objects such as images, users, tags, and groups. We have to show that our algorithms improve similarity search between object based on the potentiality and correlation between objects. VI. CONCLUSION In this paper we introduced novel similarity search using meta-path based similarity search using baseline algorithm and co-clustering based pruning algorithms to improve the similarity search based on the strengths and relationships between objects. REFERENCES [1] Jiawei Han, Lise Getoor, Wei Wang, Johannes Gehrke, Robert Grossman "Mining Heterogeneous Information Networks Principles and Methodologies" [2] Y. Koren, S.C. North, and C. Volinsky, “Measuring and Extracting Proximity in Networks,” Proc. 12th ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 245-255, 2006. [3] M. Ito, K. Nakayama, T. Hara, and S. Nishio, “Association Thesaurus Construction Methods Based on Link Co-Occurrence 321 INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT ISBN: 378 - 26 - 138420 - 5 www.iaetsd.in
- 6. Analysis for Wikipedia,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), pp. 817-826, 2008. [4] K. Nakayama, T. Hara, and S. Nishio, “Wikipedia Mining for an Association Web Thesaurus Construction,” Proc. Eighth Int’l Conf. Web Information Systems Eng. (WISE), pp. 322-334, 2007. [5] M. Yazdani and A. Popescu-Belis, “A Random Walk Framework to Compute Textual Semantic Similarity: A Unified Model for Three Benchmark Tasks,” Proc. IEEE Fourth Int’l Conf. Semantic Computing (ICSC), pp. 424-429, 2010. [6] R.L. Cilibrasi and P.M.B. Vita´nyi, “The Google Similarity Distance,” IEEE Trans. Knowledge and Data Eng., vol. 19, no. 3, pp. 370-383, Mar. 2007. [7] G. Kasneci, F.M. Suchanek, G. Ifrim, M. Ramanath, and G. Weikum, “Naga: Searching and Ranking Knowledge,” Proc. IEEE 24th Int’l Conf. Data Eng. (ICDE), pp. 953-962, 2008. [8] R.K. Ahuja, T.L. Magnanti, and J.B. Orlin, Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993. 322 INTERNATIONAL CONFERENCE ON CURRENT INNOVATIONS IN ENGINEERING AND TECHNOLOGY INTERNATIONAL ASSOCIATION OF ENGINEERING & TECHNOLOGY FOR SKILL DEVELOPMENT ISBN: 378 - 26 - 138420 - 5 www.iaetsd.in