CN105530011A - Graph data compression method and query method based on triangular statistics - Google Patents
Graph data compression method and query method based on triangular statistics Download PDFInfo
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Abstract
The invention discloses a graph data compression method based on triangular statistics, which comprises the steps of: acquiring a group of graph data, screening all triangular elements composed of three nodes having a correlation in the group of graph data, and forming a set of the triangular elements; and compressing the triangular elements into one node to obtain a compression result if more than two triangular elements comprise two identical nodes in the set. The graph data compression method increases a compression ratio by transforming an original storage form utilizing a node pair to represent a correlation between two nodes into a feature of utilizing a common node of the triangular elements. The invention further discloses an optimized query method for the graph data obtained by adopting the graph data compression method based on triangular statistics.
Description
Technical field
The invention belongs to database technical field, specifically propose a kind of diagram data compression method based on triangle statistics.
Background technology
Nowadays, diagram data has been widely used in disparate networks application, such as webpage link information, social networks and semantic network etc.But, along with the development of data scale, 1,000,000 even ten million nodes may be comprised in a diagram data, and also may there is ten million bar limit between these nodes to represent the incidence relation between these nodes.Storing and processing so large-scale diagram data is a huge challenge, and in a large-scale graph data, carry out the inquiry for relation between node or node, and its search efficiency also receives much concern.
In order to process the diagram data of the scale continued to increase more efficiently, carrying out compression to diagram data is a kind of row and effective means, significantly can reduce the storage requisite space of diagram data, and reduce read, the time cost of process and query graph data.
For the compression of large-scale diagram data, comprise the demand of following several respects:
The first, the diagram data after compression need have the characteristic of compactedness, thus reaches the object reducing memory space shared by diagram data.
The second, the compression and decompression operation for diagram data all should have high efficiency, carries out processing can not consume too much time cost in decompression step with when inquiring about for diagram data.
3rd, more preferably situation is, the diagram data after compression when for a certain class or a few class process or inquiry, need not to its carry out decompression operation can complete process or obtain required Query Result, thus improve process and search efficiency.
4th, the compression method for diagram data can realize the adjustable integralization for compression ratio and compression time cost, selects more suitably compression ratio and time cost according to practical application request.
The target of diagram data compression reduces the memory space shared by diagram data, and still can keep the various main character that artwork data has.Existing diagram data compression method can be divided into following several large class according to different basic concepts:
The first kind, based on the value of node degree in diagram data, retains and wherein has the high node of node degree and the relation between it, thus reach the object of diagram data compression.In a diagram data, the value of the degree of some nodes may not be high, is but the key event of connection two intensive subgraphs.In based on diagram data the value of node degree compression method in, in compression process, may remove node as above from diagram data, the diagram data therefore after compression will the part-structure of the original diagram data of loss.
Equations of The Second Kind, based on the similitude of node in diagram data, is combined as a new node, and integrates being attached thereto the limit connect by the node in diagram data with similar neighborhood nodal set or analog structure character.In these class methods, the difference on the limit connected between the otherness of each node in similar node set or node can be described the cost outside occupying volume.The similar tolerance allowed in this type of compression method is larger, and taking up room that so difference describes is more, and compression ratio is poorer; And the similar tolerance that compression method allows is less, then the similar node that can find is fewer, and compression ratio also can not be good; And find a setting comparatively balanced to be very difficult.
3rd class, based on the frequency that node in diagram data occurs, gives the less label of the higher node of the frequency of occurrences in diagram data or takies the less mark of memory space.These class methods need when compressing to use a large amount of system resource to carry out sorting operation, and the average time complexity of sort algorithm best is at present O (nlog
2n), space complexity is O (1), when compressing for large-scale graph data, is also undesirable in compression time.
4th class, based on the data storage format of the link information in diagram data between node and node, as adjacency list (AdjacencyList), relies on the mutation of different file layouts, directly reduces the memory space of expressing needed for same information.Better simply adjacency list compression method cannot obtain good compression ratio, the cost and consumption more decompresses when decompressing to it by more complicated adjacency list compression method.
In order to the defect overcoming the non-destructive of diagram data in prior art, compression time, compression ratio cannot be taken into account, propose a kind of diagram data compression method based on triangle statistics and querying method.This method is while guarantee graph data structure non-destructive, and in compression time and compression ratio, compare other compression method also better.Meanwhile, for the inquiry based on the diagram data after this method compression, its search efficiency also shows better.
Summary of the invention
The present invention proposes a kind of diagram data compression method based on triangle statistics, comprise the steps:
Step one: obtain picture group data, screening is wherein all to be mutually related the triangular element that node forms by three existence, forms the set of triangular element;
Step 2: in described set, if mutually jointly have two identical nodes before two or more triangular element, is then compressed into a node by described triangular element, obtains compression result.
In the described diagram data compression method based on triangle statistics that the present invention proposes, in described step one, the upper bound of the degree of setting node and lower bound are for screening the node of described diagram data, filter out from the node be between the described upper bound and lower bound and to be allly mutually related the triangular element that node forms by three existence, form the set of triangular element.
In the described diagram data compression method based on triangle statistics that the present invention proposes, the described upper bound is more than 100, and the span of described lower bound is 2-10.
In the described diagram data compression method based on triangle statistics that the present invention proposes, in described step one, carry out inverted order statistics to all nodes, described inverted order statistics comprises the steps:
Step a: nodes all in diagram data are carried out descending sort by the degree of node, in order to optimize squeeze operation execution efficiency;
Step b: for each node creates an empty set;
Step c: the neighbor node node of in diagram data and node degree being less than to this node, if set A | u| ∩ A|v| is not empty, then triangular element represents with following formula:
T＝{(u，v，w)|w∈{A|u|∩A|v|}}；
Wherein, u represents current calculating crunode, v represents the neighbor node of u, A represents that all node degree of a node are greater than the neighbor node set of current calculating crunode, A|u| represents that all node degree are greater than the neighbor node set of the u of u, A|v| represents that all node degree are greater than the neighbor node set of the v of u, and w represents that node degree is greater than u, and is the neighbor node of node u and node v simultaneously;
Steps d: repeating said steps c, till each node in traversing graph data, obtains the set of all triangular element in figure.
In the described diagram data compression method based on triangle statistics that the present invention proposes, described diagram data is for comprising social networks diagram data, web page interlinkage diagram data and papers quoted diagram data.
The invention allows for the querying method of a kind of process based on the diagram data of triangle statistical chart data compression process, comprise the steps:
Whether step 1: for the inquiry request of a node, exist the triangular element of mating completely with described node or the triangular element comprising described node in the set of scanning triangular element; If exist, then return described node and common neighbor node thereof, if do not exist, then carry out step 2;
Step 2: if described node is present in two or more triangular element, then add up the information of the neighbor node of described triangular element, and the common factor finally returning the information of described neighbor node returns as Query Result;
Step 3: if there is not the node mated with inquiry request in diagram data, then return empty set.For the querying method of gained diagram data after the diagram data compression method compression through adding up based on triangle in the present invention, on the basis of diagram data or partial decompressing diagram data of not decompressing, query manipulation can be carried out to it.Compare the compression method that other just must can perform data query after decompression diagram data, diagram data compression method proposed by the invention be supported in do not decompress or partial decompressing diagram data in perform a part query manipulation.Decompression diagram data will take a large amount of query time, and therefore, the present invention can significantly improve search efficiency in diagram data upon compression by avoiding decompression operation.
The querying method proposed due to the present invention be supported in do not decompress or partial decompressing diagram data in perform the query manipulation of common neighbor node, when therefore performing the inquiry to the common neighbor node of two nodes any in diagram data, its search efficiency will be 2 times or higher of querying method based on diagram data after decompressing.Below some concept and the definition relevant with the present invention are described.
The compression ratio of diagram data compression: the memory space shared by uncompressed diagram data and the ratio compressing rear memory space shared by diagram data.
The compression time of diagram data compression: the total time that squeeze operation uses is carried out to diagram data.
The degree of node in diagram data: all quantity being connected to the limit of certain node i.e. degree of node for this reason in diagram data.
Beneficial effect of the present invention comprises: the present invention by original node that utilizes to the file layout expressing incidence relation between two nodes, change the characteristic utilizing triangular element to share node into, by multiple triangular element of new file layout Storage sharing two nodes, compare existing compression algorithm, improve compression efficiency and be about 5-30%.The present invention is while compressing diagram data, and the execution efficiency that can also operate the partial query for diagram data promotes to some extent, raises the efficiency more than 50%, on average raise the efficiency and be about 10-20% under optimal situation.
Accompanying drawing explanation
Fig. 1 is the flow chart of the diagram data compression method that the present invention is based on triangle statistics.
Fig. 2 is the flow chart of the graph data query method that the present invention is based on triangle statistics.
Fig. 3 is the schematic diagram of the former data structure in an embodiment before compression.
Fig. 4 is the schematic diagram of data structure after compression in an embodiment.
Embodiment
In conjunction with following specific embodiments and the drawings, the present invention is described in further detail.Implement process of the present invention, condition, experimental technique etc., except the following content mentioned specially, be universal knowledege and the common practise of this area, the present invention is not particularly limited content.
As Fig. 1, the present invention is based on the diagram data compression method of triangle statistics, comprise the steps:
Step one: obtain picture group data, screening is wherein all to be mutually related the triangular element that node forms by three existence, forms the set of triangular element;
Step 2: in set, if mutually jointly have two identical nodes before two or more triangular element, is then compressed into a node, obtains compression result by triangular element.
Wherein, in step one further by the upper bound of degree of setting node and lower bound for screening the node of diagram data, all quantity being connected to the limit of certain node i.e. degree of node for this reason in diagram data.Filter out the node meeting the upper bound and lower bound, filter out from above-mentioned node and to be mutually related the triangular element that node forms by three existence, form the set of triangular element.By setting the different upper bounds and lower bound numerical value, thus reduce the time overhead of statistics triangular element.The numerical value in the upper bound is configured to more than 100 or 100, is set between 100-200 by the numerical value in the upper bound in the present embodiment.Because a node at least needs two neighbor nodes mutually could form a triangular element, so the minimum value of lower bound is 2.Preferably, the number range of lower bound is set to 2-10, when lower bound maximum occurrences higher than 10 may reduce to inquiry effect of optimization.Known through testing, make former time overhead be reduced by least 500% by the setting adding the upper bound and lower bound.
The present invention be different from generally use based on node enumerate statistic law and based on node right enumerate statistic law, employ the more efficient inverted order statistical method of one, significantly reduce time overhead.Inverted order statistical method can also reduce the time overhead of statistics triangular element further on the basis setting the upper bound and lower bound.
The encode implementation of the diagram data compression realized based on triangle statistical method is described with following table 1.
First the degree of the node in diagram data by node is sorted, to reduce the time cost of follow-up triangle statistical computation.And the distribution situation of degree according to node in diagram data, and for the real needs of figure, the bound of the degree of the node used when determining that triangle is added up.See row 2 to row 10 in table 1, the function that this false code realizes is the node for each meets this boundary, creates an empty set, is denoted as A.The function that false code in row 11 to row 23 realizes is the node each being met to this boundary, be denoted as u, and degree is less than his neighbor node, be denoted as v, if there is set A | u| ∩ A|v| is not for empty, then T={ (u, v, w) | { A|u| ∩ A|v|}} is required triangle to w ∈.Finally node u is joined in A|v|, i.e. A|v|=A|v| ∪ u.Then, constantly repeat following steps, until each node in traversing graph data.Finally, the diagram data compression result realized based on triangle statistical method can be obtained.
Table 1 realizes the code segment that the present invention is based on the diagram data compression that triangle statistical method realizes
See Fig. 3, wherein Fig. 3 display is a diagram data example, utilizes the diagram data compression method that the present invention is based on triangle statistics to compress this diagram data.In compression process, first in triangle statistics, filter the node 1 with the highest node degree.Retrieve the node 3 that the node 2 wherein meeting the upper bound and lower bound condition and its neighbor node moderate read at first are less than this node afterwards, retrieve node 2 and node 3 have common neighbor node collection { 4,5,6,11} meets contractive condition, thus generate new triangular element T={ (2,3), { 4,5,6,11}}.See Fig. 4, the node 2 after compression and node 3 merge into a node.The like, in traversing graph data after each node, can obtain final compression result, diagram data is after compression as shown in Figure 4.Also without any the triangular element be made up of node 1 in diagram data after compression, and original 12 nodes have been reduced to 9 by final compression result, and the sum on limit between node has been reduced to 13 from 29.Diagram data after the visible the present invention of utilization compresses, while reaching the compression ratio of nearly 50%, maintains the non-destructive of original diagram data.
As shown in Figure 2, the present invention is based on the graph data query method of triangle statistics, comprise the steps:
Whether step 1: for the inquiry request of a node, exist the triangular element of mating completely with node or the triangular element comprising node in the set of scanning triangular element; If exist, then return node and common neighbor node thereof, if do not exist, then carry out step 2;
Step 2: if node is present in two or more triangular element, then add up the information of the neighbor node of triangular element, and the common factor finally returning the information of neighbor node returns as Query Result;
Step 3: if there is not the node mated with inquiry request in diagram data, then return empty set.
It is characterized in that, due to its be supported in do not decompress or partial decompressing diagram data in perform the query manipulation of common neighbor node, when therefore performing the inquiry to the common neighbor node of two nodes any in diagram data, its search efficiency will be 2 times or higher of querying method based on diagram data after decompressing.Our experiments show that, in many cases, on the figure compressed based on triangle statistical method, do not need the whole figure of decompress(ion) can obtain common neighbor node collection, decreased average 10-20% query time.
The encode implementation of carrying out common neighbor queries method on the figure compressed based on triangle statistical method is described in detail with following table 2.Wherein see row 2 to row 3, whether the function that this section of false code realizes retrieves in diagram data upon compression to have coupling institute completely to inquire about the right triangular element of node, if existence, directly exports Query Result.The function that the false code of row 5 to row 12 realizes be in the triangular element after checking compression in diagram data whether by with the triangular element of inquiring about the node that matches and another node and forming, and extract the information of its neighbor node, i.e. partial decompressing operation, thus return Query Result at short notice.
Consult Fig. 4, carrying out inquiry in the diagram data after compression is example, and as inquiry is the common neighbor node of node 2 and node 3, then can directly read by node 2,3 composition triangular element and when not decompressing diagram data fast return result set { Isosorbide-5-Nitrae, 5,6,11}.And when inquiring about the common neighbor node of node 4 and node 7, owing to not existing by node 4, the triangular element of 7 compositions, then need to operate by partial decompressing the neighbor node collection reading node 4 and node 7 respectively, and { 1,10} returns as a result to get their common factor.Owing to now only needing partial decompressing diagram data, its search efficiency is also very high.
Table 2 realizes the code segment that the present invention is based on the diagram data inquiry that triangle statistical method realizes
Protection content of the present invention is not limited to above embodiment.Under the spirit and scope not deviating from inventive concept, the change that those skilled in the art can expect and advantage are all included in the present invention, and are protection range with appending claims.
Claims (6)
1., based on a diagram data compression method for triangle statistics, it is characterized in that, comprise the steps:
Step one: obtain picture group data, screening is wherein all to be mutually related the triangular element that node forms by three existence, forms the set of triangular element;
Step 2: in described set, if mutually jointly have two identical nodes before two or more triangular element, is then compressed into a node by described triangular element, obtains compression result.
2. as claimed in claim 1 based on the diagram data compression method of triangle statistics, it is characterized in that, in described step one, the upper bound of the degree of setting node and lower bound are for screening the node of described diagram data, filter out from the node be between the described upper bound and lower bound and to be allly mutually related the triangular element that node forms by three existence, form the set of triangular element.
3., as claimed in claim 2 based on the diagram data compression method of triangle statistics, it is characterized in that, the described upper bound is more than 100, and the span of described lower bound is 2-10.
4. as claimed in claim 1 or 2 based on the diagram data compression method of triangle statistics, it is characterized in that, in described step one, carry out inverted order statistics to all nodes, described inverted order statistics comprises the steps:
Step a: nodes all in diagram data are carried out descending sort by the degree of node, in order to optimize squeeze operation execution efficiency;
Step b: for each node creates an empty set;
Step c: the neighbor node node of in diagram data and node degree being less than to this node, if set A | u| ∩ A|v| is not empty, then triangular element represents with following formula:
T＝{(u，v，w)|w∈{A|u|∩A|v|}}；
Wherein, u represents current calculating crunode, v represents the neighbor node of u, A represents that all node degree of a node are greater than the neighbor node set of current calculating crunode, A|u| represents that all node degree are greater than the neighbor node set of the u of u, A|v| represents that all node degree are greater than the neighbor node set of the v of u, and w represents that node degree is greater than u, and is the neighbor node of node u and node v simultaneously;
Steps d: repeating said steps c, till each node in traversing graph data, obtains the set of all triangular element in figure.
5., as claimed in claim 1 based on the diagram data compression method of triangle statistics, it is characterized in that, described diagram data is for comprising social networks diagram data, web page interlinkage diagram data and papers quoted diagram data.
6. process is based on a querying method for the diagram data of triangle statistical chart data compression process, it is characterized in that, comprises the steps:
Whether step 1: for the inquiry request of a node, exist the triangular element of mating completely with described node or the triangular element comprising described node in the set of scanning triangular element; If exist, then return described node and common neighbor node thereof, if do not exist, then carry out step 2;
Step 2: if described node is present in two or more triangular element, then add up the information of the neighbor node of described triangular element, and the common factor finally returning the information of described neighbor node returns as Query Result;
Step 3: if there is not the node mated with inquiry request in diagram data, then return empty set.
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Application publication date: 20160427 |