You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Summarizing semantic graphs: a survey 
Chapter Title:  Non-quotient graph summaries 
1. Abstract of Cited Paper (BIB001):  We propose a highly compact two-part representation of a given graph G consisting of a graph summary and a set of corrections. The graph summary is an aggregate graph in which each node corresponds to a set of nodes in G, and each edge represents the edges between all pair of nodes in the two sets. On the other hand, the corrections portion specifies the list of edge-corrections that should be applied to the summary to recreate G. Our representations allow for both lossless and lossy graph compression with bounds on the introduced error. Further, in combination with the MDL principle, they yield highly intuitive coarse-level summaries of the input graph G. We develop algorithms to construct highly compressed graph representations with small sizes and guaranteed accuracy, and validate our approach through an extensive set of experiments with multiple real-life graph data sets. To the best of our knowledge, this is the first work to compute graph summaries using the MDL principle, and use the summaries (along with corrections) to compress graphs with bounded error. 
2. Abstract of Cited Paper (BIB002):  Graphs are widely used to model real world objects and their relationships, and large graph datasets are common in many application domains. To understand the underlying characteristics of large graphs, graph summarization techniques are critical. However, existing graph summarization methods are mostly statistical (studying statistics such as degree distributions, hop-plots and clustering coefficients). These statistical methods are very useful, but the resolutions of the summaries are hard to control. In this paper, we introduce two database-style operations to summarize graphs. Like the OLAP-style aggregation methods that allow users to drill-down or roll-up to control the resolution of summarization, our methods provide an analogous functionality for large graph datasets. The first operation, called SNAP, produces a summary graph by grouping nodes based on user-selected node attributes and relationships. The second operation, called k-SNAP, further allows users to control the resolutions of summaries and provides the "drill-down" and "roll-up" abilities to navigate through summaries with different resolutions. We propose an efficient algorithm to evaluate the SNAP operation. In addition, we prove that the k-SNAP computation is NP-complete. We propose two heuristic methods to approximate the k-SNAP results. Through extensive experiments on a variety of real and synthetic datasets, we demonstrate the effectiveness and efficiency of the proposed methods. 
3. Abstract of Cited Paper (BIB003):  Large graph databases are commonly collected and analyzed in numerous domains. For reasons related to either space efficiency or for privacy protection (e.g., in the case of social network graphs), it sometimes makes sense to replace the original graph with a summary, which removes certain details about the original graph topology. However, this summarization process leaves the database owner with the challenge of processing queries that are expressed in terms of the original graph, but are answered using the summary. In this paper, we propose a formal semantics for answering queries on summaries of graph structures. At its core, our formulation is based on a random worlds model. We show that important graph-structure queries (e.g., adjacency, degree, and eigenvector centrality) can be answered efficiently and in closed form using these semantics. Further, based on this approach to query answering, we formulate three novel graph partitioning/compression problems. We develop algorithms for finding a graph summary that least affects the accuracy of query results, and we evaluate our proposed algorithms using both real and synthetic data. 
4. Abstract of Cited Paper (BIB004):  It is common to find graphs with millions of nodes and billions of edges in, e.g., social networks. Queries on such graphs are often prohibitively expensive. These motivate us to propose query preserving graph compression, to compress graphs relative to a class Λ of queries of users' choice. We compute a small Gr from a graph G such that (a) for any query Q E Λ Q, Q(G) = Q'(Gr), where Q' E Λ can be efficiently computed from Q; and (b) any algorithm for computing Q(G) can be directly applied to evaluating Q' on Gr as is. That is, while we cannot lower the complexity of evaluating graph queries, we reduce data graphs while preserving the answers to all the queries in Λ. To verify the effectiveness of this approach, (1) we develop compression strategies for two classes of queries: reachability and graph pattern queries via (bounded) simulation. We show that graphs can be efficiently compressed via a reachability equivalence relation and graph bisimulation, respectively, while reserving query answers. (2) We provide techniques for aintaining compressed graph Gr in response to changes ΔG to the original graph G. We show that the incremental maintenance problems are unbounded for the two lasses of queries, i.e., their costs are not a function of the size of ΔG and changes in Gr. Nevertheless, we develop incremental algorithms that depend only on ΔG and Gr, independent of G, i.e., we do not have to decompress Gr to propagate the changes. (3) Using real-life data, we experimentally verify that our compression techniques could reduce graphs in average by 95% for reachability and 57% for graph pattern matching, and that our incremental maintenance algorithms are efficient. 
5. Abstract of Cited Paper (BIB005):  Graph summarization is valuable approach to analyze various real life phenomenon, like communities, influential nodes, and information flow in a big graph. To summarize a graph, nodes having similar neighbors are merged into super nodes and their corresponding edges are compressed into super edges. Existing methods find similar nodes either by nodes ordering or perform pairwise similarity computations. Compression-by-node ordering approaches are scalable but provide lesser compression due to exhaustive similarity computations of their counterparts. In this paper, we propose a novel set-based summarization approach that directly summarizes naturally occurring sets of similar nodes in a graph. Our approach is scalable since we avoid explicit similarity computations with non-similar nodes and merge sets of nodes in each iteration. Similarly, we provide good compression ratio as each set consists of highly similar nodes. To locate sets of similar nodes, we find candidate sets of similar nodes by using locality sensitive hashing. However, member nodes of every candidate set have varying similarities with each other. Therefore, we propose a heuristic based on similarity among degrees of candidate nodes, and a parameter-free pruning technique to effectively identify subset of highly similar nodes from candidate nodes. Through experiments on real world graphs, our approach requires lesser execution time than pairwise graph summarization, with margin of an order of magnitude in graphs containing nodes with highly diverse neighborhood, and produces summary at similar accuracy. Similarly, we observe comparable scalability against the compression-by-node ordering method, while providing better compression ratio. 
6. Abstract of Cited Paper (BIB006):  We study the problem of graph summarization. Given a large graph we aim at producing a concise lossy representation (a summary) that can be stored in main memory and used to approximately answer queries about the original graph much faster than by using the exact representation. In this work we study a very natural type of summary: the original set of vertices is partitioned into a small number of supernodes connected by superedges to form a complete weighted graph. The superedge weights are the edge densities between vertices in the corresponding supernodes. To quantify the dissimilarity between the original graph and a summary, we adopt the reconstruction error and the cut-norm error. By exposing a connection between graph summarization and geometric clustering problems (i.e., k-means and k-median), we develop the first polynomial-time approximation algorithms to compute the best possible summary of a certain size under both measures. We discuss how to use our summaries to store a (lossy or lossless) compressed graph representation and to approximately answer a large class of queries about the original graph, including adjacency, degree, eigenvector centrality, and triangle and subgraph counting. Using the summary to answer queries is very efficient as the running time to compute the answer depends on the number of supernodes in the summary, rather than the number of nodes in the original graph.

Literature Review:
There are also many methods that construct non-quotient graph summaries. They distinguish nodes according to several criteria/measures and create summaries in which summary nodes represent multiple nodes out of the original graph. A Dataguide BIB001 (see Fig. 5 for an example) is a summary of a directed acyclic graph, having one node for each data path in the original graph. A graph node reachable by a set of paths belongs to the extents of all the respective Dataguide nodes. Thus, given a path query which may also contain wildcards, and a Dataguide, it is easy to identify the Dataguide nodes corresponding to the query, and from there their extents. The authors show how to integrate the Dataguide path index with more conventional indexes, e.g., a value index which gives access to all the nodes containing a certain constant value etc. A Dataguide is not a quotient: an input node may be represented by two Dataguide nodes, if it is reachable by two distinct paths in the input graph. Building a Dataguide out of a data graph amounts to determinizing an undeterministic finite automaton; the worst-case complexity of this is known to be exponential in the size of the input graph, yet only linear when the database is tree-structured. Summary-based query answering with an acceptable level of error is considered in BIB006 BIB002 . The focus is on graph compression while preserving bounded-error query answering and/or the ability to fully reconstruct the graph from the summary with the help of so-called "corrections" (i.e., edges to add to or remove from the "expansion" of the summary into the regular part of the graph it derives from). The nodes of the resulting structural summary represent partitions of similar nodes from G, while a summary edge exists between two summary nodes u and v only if the nodes from G represented by u and v are densely connected. Similarly, LeFevre and Terzi BIB004 aims to compress G, but without considering corrections, while their edge labels represent the number of edges within each partition set, and the number of edges between every two such sets. To determine similar nodes, Khan et al. BIB006 relies on locality-sensitive hashing, while BIB004 BIB002 use a clustering method where pairs of nodes to merge are chosen based on the optimal value of an objective function. In BIB007 , the authors build on the concepts of BIB004 to show how to obtain in polynomial time, summaries which are close to the optimal one (in terms of corrections needed. However, these works focus on node connectivity, and ignore the node and edge labels which crucially encode the data content of an RDF graph. Tian et al. BIB003 proposes the SNAP (Summarization on Grouping Nodes on Attributes and Pairwise Relationships) technique, whose purpose is to construct, with some user input, a summary graph that can be used for visualization. A SNAP summary represents all the input graph nodes and edges: its nodes forms a partition of the input graph nodes, and there is an edge of type t between two summary nodes A and B if and only if some input graph node represented by A is connected through an edge of type t to an input graph node represented by B. Further, a SNAP summary has a minimal number of nodes such that (i) all input graph nodes represented by a summary node have same values for some user-selected attributes and (ii) every input graph node represented by some summary node is connected to some input graph nodes through edges of some user-selected types. Fan et al. BIB005 considers reachability and graph pattern queries on labeled graphs, and builds answer-preserving summaries for such queries, that is: for a given graph G, summary S(G) and query Q, there exists a query Q which can be computed from Q, and a post-processing procedure P such that P(Q (S(G))) = Q(G). In other words, evaluating Q on the summary and then applying the post-processing P leads to the result Q(G). This property is rather strong; however, it is attained not under the usual query semantics based on graph homomorphism, underlying SPARQL, but under a bounded graph simulation one. Under these semantics, answering a query becomes P (instead of N P), at the price of not preserving the query structure (i.e., joins). The authors propose two parallel graph compression strategies, targeting different kinds of queries: (i) reachability queries, where we seek to know if one node is reachable from another; (ii) graph pattern queries, for which they attain a significant compression ratio. It should be again stressed that the authors consider these queries under non-standard, more lenient semantics than the ones used for RDF querying. The complexity of building their summaries are O(N * M) for reachability queries, and O(N * log(M)) for graph pattern queries.