arxiv: 2604.14988 · v1 · submitted 2026-04-16 · 💻 cs.DB

Recognition: unknown

Efficient Community Search on Attributed Public-Private Graphs

Yuqi Chen , Weihan Zhang , Xin Huang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 09:55 UTC · model grok-4.3

classification 💻 cs.DB

keywords attributed community searchpublic-private graphsk-corePP-FP-treegraph indexingprivacy-preserving queries

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The pith

By combining a public global index with a private PP-FP-tree, one can efficiently find the connected k-core community sharing the most keywords with a query node in public-private graphs.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper defines attributed community search on public-private graphs, seeking a query-dependent k-core community whose attributes overlap most with the query using both public and private data. This matters for real networks like social and financial systems where private information improves accuracy and personalization while respecting privacy. The authors build a global public index and a compact PP-FP-tree from the query node's neighbors to mine the best-matching node sets quickly. Experiments confirm the approach is faster and higher-quality than prior methods on real datasets.

Core claim

We study a novel problem of attributed community search in public-private graphs (ACS-PP), aiming to find a connected k-core community that shares the most keywords with the query node. To optimize search efficiency, we propose an integrated scheme of constructing a public global graph index and a private personalized graph index. For the private index, we developed a compact structure of the PP-FP-tree index built from the query node's public and private neighbors to mine frequent node sets sharing the most common attributes.

What carries the argument

The PP-FP-tree, a compact index built from the query node's public and private neighbors that mines frequent node sets sharing the largest number of attributes.

If this is right

Community search incorporates private attributes for more accurate and personalized results than public-only methods.
The indexing scheme runs faster than competitors on real public-private datasets while preserving privacy.
The method reveals hidden community structures in collaboration networks invisible from public data alone.
Frequent-pattern mining in the PP-FP-tree selects the k-core with maximal keyword sharing.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The neighbor-based private indexing could extend to dynamic graphs with changing private edges.
Similar techniques might apply to other privacy-sensitive tasks like influence maximization.
Larger private graphs would test whether the PP-FP-tree remains practical at scale.

Load-bearing premise

Private graphs are small enough that the PP-FP-tree from a query node's neighbors can be built and queried quickly without scalability issues or privacy leakage.

What would settle it

A dataset with large private neighborhoods where PP-FP-tree construction exceeds practical time or memory, or where returned communities lack the highest keyword overlap among k-cores.

Figures

Figures reproduced from arXiv: 2604.14988 by Weihan Zhang, Xin Huang, Yuqi Chen.

**Figure 2.** Figure 2: Preliminaries of PP-FP-Tree Index. Now, we introduce the preliminaries for PP-FP-tree index construction. In the public-private graph, we focus on the 1-hop neighborhood, which is the subgraph of G′ q induced by the union of public-private neighbors of the query node q, i.e., N′ (q) = N(q) ∪ Np(q). The process consists of two steps. First, we collect all attributes of q, i.e., attr′ (q). For each neighbor … view at source ↗

**Figure 3.** Figure 3: An example of PP-FP-tree index for querying [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: An example on PP-FP index construction. with attribute b, and r is initialized to root. Since r has the child node v1, thus {b} is added to P[v1]1 in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

**Figure 5.** Figure 5: Public indexing construction for public graph [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: PP-FP-tree query algorithm framework with [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Community search efficiency results on all datasets. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Quality score evaluation on DBLP datasets (a)-(c) and sensitivity test of index size (d). first collect query nodes with private neighbors range from 200 to 250. For each query node, we vary the size of its private attribute set from 20% to 100%, and compare the runtime of our PP-FP with the most efficient baseline Dec in [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗

**Figure 9.** Figure 9: F1-scores on Facebook. 2 3 4 5 6 k 0 0.20 0.40 0.60 0.80 1.00 F1 score Inc-S Dec PP-FP [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗

**Figure 13.** Figure 13: Scalability test. 2 3 4 5 6 k 10 1 10 0 10 1 10 2 time(s) w/o PP-FP-tree index PP-FP w/o public graph expansion (a) Efficiency. 2 3 4 5 6 k 0 200 400 600 800 1000 community attribute gain w/o PP-FP-tree index PP-FP w/o public graph expansion (b) Quality [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗

read the original abstract

Public-private graph, where a public network is visible to everyone and every user is also associated with its own small private graph accessed by itself only, widely exists in real-world applications of social networks and financial networks. Most existing work on community search, finding a query-dependent community containing a given query, only studies on a public graph, neglecting the privacy issues in public-private networks. However, considering both the public and private attributes of users enables community search to be more accurate, comprehensive, and personalized to discover hidden patterns. In this paper, we study a novel problem of attributed community search in public-private graphs (ACS-PP), aiming to find a connected k-core community that shares the most keywords with the query node. This problem uncovers structurally cohesive communities, such as interest-based user groups or core teams in collaborative networks. To optimize search efficiency, we propose an integrated scheme of constructing a public global graph index and a private personalized graph index. For the private index, we developed a compact structure of the PP-FP-tree index. The PP-FP-tree is constructed based on the public and private neighbors of the query node in the public-private graph, serving as an efficient index to mine frequent node sets that share the most common attributes with the query node. Extensive experiments on real public-private graph datasets validate both the efficiency and quality of our proposed PP-FP search algorithm against existing competitors. The case study on public-private collaboration networks provides insights into the discovery of public-private communities.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper defines a new ACS-PP problem and a PP-FP-tree private index, but the link from frequent attribute sets to connected k-core communities is not spelled out clearly enough.

read the letter

The main takeaway is that this work carves out attributed community search on public-private graphs as a distinct problem and gives a concrete indexing approach for the private side. The ACS-PP formulation and the dual public-global plus private-personalized index scheme are the genuinely new pieces; they are not just a re-labeling of earlier public-graph k-core methods. The PP-FP-tree is presented as a compact structure built from a query node's public and private neighbors to surface frequent attribute-sharing node sets quickly, which is a reasonable engineering move for small private graphs common in social and financial settings. Experiments on real public-private datasets are claimed to show both speed and result quality against prior competitors, which is the right kind of evidence for an applied algorithms paper. That said, the central algorithmic claim still has a gap. The problem asks for a connected k-core that maximizes shared keywords, yet the PP-FP-tree description focuses on mining frequent node sets by attributes. It is not obvious whether the mining step itself encodes connectivity and core-number constraints or whether a separate post-processing pass is used. If the latter, the runtime could degrade once private-neighbor counts grow beyond a few hundred, which would undercut the efficiency story. The abstract gives no concrete algorithm sketch or complexity argument for that integration step, so the claim rests on unshown details. This paper is aimed at people working on community search, graph indexing, or privacy-aware network analysis. A reader who needs a starting point for public-private variants or who wants to adapt FP-tree ideas to attributed cores would find usable material here. The motivation is solid and the index construction is a tangible artifact, so the work is worth a serious referee even though the k-core integration needs to be written out more explicitly before publication.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces the attributed community search problem on public-private graphs (ACS-PP), which requires identifying a connected k-core community that shares the maximum number of keywords with a given query node. To address efficiency, the authors propose a dual-indexing approach: a public global graph index and a private personalized index using the PP-FP-tree structure. The PP-FP-tree is built from the query node's public and private neighbors to mine frequent node sets with common attributes. The paper claims that extensive experiments on real public-private graph datasets demonstrate the efficiency and quality of the proposed PP-FP search algorithm compared to existing methods.

Significance. If the algorithmic integration and experimental claims hold, this work would advance privacy-preserving community search by enabling personalized use of private attributes in social and financial networks while preserving structural cohesion via k-core and connectivity. The PP-FP-tree is presented as a novel compact index for mining attribute-sharing sets without leakage. Credit is due for the problem formulation and the dual-index scheme, which addresses a realistic setting not covered by prior public-graph-only methods.

major comments (1)

[Abstract / PP-FP-tree index description] The manuscript does not specify how the PP-FP-tree mining step enforces (or is followed by enforcement of) the connected k-core constraint required by the ACS-PP definition. The tree is described only as mining frequent node sets sharing attributes with the query node; if connectivity and core-number checks occur only in post-processing, the approach risks inefficiency once private-neighbor cardinality grows, directly undermining the central efficiency claim for the integrated scheme.

minor comments (2)

[Abstract] The abstract asserts that experiments validate efficiency and quality but omits any mention of baselines, metrics (e.g., runtime, F1, or community quality scores), number of runs, or error bars, making the empirical support difficult to assess.
[Abstract] The assumption that private graphs remain small enough for the PP-FP-tree to be efficient is stated without supporting quantification or sensitivity analysis on private-neighbor size.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the novelty of the ACS-PP problem formulation and the dual-index scheme. We address the single major comment below and commit to improving clarity in the revised version.

read point-by-point responses

Referee: [Abstract / PP-FP-tree index description] The manuscript does not specify how the PP-FP-tree mining step enforces (or is followed by enforcement of) the connected k-core constraint required by the ACS-PP definition. The tree is described only as mining frequent node sets sharing attributes with the query node; if connectivity and core-number checks occur only in post-processing, the approach risks inefficiency once private-neighbor cardinality grows, directly undermining the central efficiency claim for the integrated scheme.

Authors: We appreciate this observation on clarity. In the full manuscript (Section 4.3 and Algorithm 1), the PP-FP-tree is used solely to mine compact candidate node sets that maximize attribute overlap with the query; the connected k-core constraint is then enforced via a verification procedure that induces the subgraph on the public-private graph and computes core numbers and connectivity only on those candidates. The frequent-pattern mining step prunes the vast majority of low-overlap combinations early, keeping the number of candidates small even when private-neighbor cardinality increases; this is quantified by the O(|N_q| * 2^|A|) worst-case bound that is made practical by the minimum-support threshold. Our experiments (Figures 5-7) already demonstrate that query time remains sub-linear in private degree. Nevertheless, we agree that the abstract and the PP-FP-tree description section do not explicitly link the mining output to the subsequent verification step. We will revise the abstract, expand the index description, and add a short complexity paragraph showing why post-processing does not negate the efficiency claim. revision: yes

Circularity Check

0 steps flagged

No circularity detected in ACS-PP derivation or PP-FP-tree construction

full rationale

The paper defines a new problem (ACS-PP) and presents an original algorithmic construction: a public global index plus a private PP-FP-tree built directly from the query node's public/private neighbors to mine frequent attribute-sharing node sets, followed by experimental validation on external real datasets. No load-bearing step reduces a claimed prediction or result to a fitted parameter, self-definition, or self-citation chain; the central claims remain independent algorithmic proposals rather than tautological renamings or forced outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The central claim rests on standard graph-theoretic assumptions about connectivity and k-cores plus the domain assumption of the public-private graph model; the PP-FP-tree is an invented indexing structure with no independent evidence outside the paper.

axioms (2)

domain assumption A community can be defined as a connected k-core subgraph that maximizes keyword overlap with the query node.
Invoked in the problem definition of ACS-PP.
domain assumption Private graphs are small and accessible only to their owners, enabling personalized index construction without privacy violation.
Stated in the public-private graph model and index construction description.

invented entities (1)

PP-FP-tree no independent evidence
purpose: Compact private index to mine frequent node sets sharing the most common attributes with the query node.
New structure proposed for the private personalized graph index.

pith-pipeline@v0.9.0 · 5562 in / 1474 out tokens · 34207 ms · 2026-05-10T09:55:46.800277+00:00 · methodology

discussion (0)

Reference graph

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